MD Amal Matar
email: amal.matar@crb.uu.se

Mats Hansson

Santa Slokenberga

Adam Panagiotopoulos

| Gauthier

Olga Tzortzatou

Kärt Pormeister

Elias Uhlin

Antonella Cardone

Michael Beauvais

Amal Orcid

Matar

Gauthier Chassang

A proposal for an international Code of Conduct for data sharing in genomics

Keywords: biobanking, cloud computing, code of conduct, data protection, genomics, international data transfers, privacy

As genomic research becomes commonplace across the world, there is an increased need to coordinate practices among researchers, especially with regard to data sharing. One such way is an international code of conduct. In September 2020, an expert panel consisting of representatives from various fields convened to discuss a draft proposal formed via a synthesis of existing professional codes and other recommendations. This article presents an overview and analysis of the main issues related to international genomic research that were discussed by the expert panel, and the results of the discussion and follow up responses by the experts. As a result, the article presents as an annex a proposal for an international code of conduct for data sharing in genomics that is meant to establish best practices.

In response to the recognized need for a CoC, the SIENNA (Stakeholder-Informed Ethics for New technologies with high socio-ecoNomic and human rights impAct) project convened an expert panel consisting of representatives from various fields to discuss a draft CoC proposal, which was formed via a synthesis of existing professional codes of conduct. While our CoC is not a GDPR one in the sense of article 40 GDPR, understanding the data protection issues that genomic and health-related research pose in an international context was largely conducted through a GDPR lens.

That is to say, notions of identifiability, legal basis for data processing, transfer rules, etc. featured prominently in the conversation. This is principally due to composition of the expert group, which was comprised of individuals mainly based in Europe. Even those from other continents are involved in European data protection issues and thus the GDPR provided a common language for understanding certain issues. This is a strength when one considers the GDPR's global impact. Indeed, more broadly, the GDPR may be understood as a sophisticated, more comprehensive version of the fair information principles, which are the basis for much of data protection and privacy law the world over. 6 However, the reliance on the GDPR as a prism to understand issues is also a weakness because other jurisdictions may have different ways of conceptualizing data protection and privacy issues, and therefore the CoC may prove more difficult to adapt to local circumstances.

| THE GDPR, INTERNATIONAL DATA TRANSFERS AND GENOMIC DATA

One of the problems pertaining to international data sharing in genomics are the differing levels of data protection and privacy rules across the globe. Within the European Economic Area (EU & Norway, Iceland, Lichtenstein), the GDPR creates a directly applicable framework for the processing of personal data, and thus, of genomic data. However, at the same time, it co-exists with the national and international legal frameworks regulating both, the processing of health and genetic data as well as scientific research. Health-related data generally and genomic data specifically are considered "specialcategory" sensitive data and therefore warrant special protections. However, if these data are used for research purposes, there is some flexibility to the generally more stringent rules concerning sensitive data. 7 In the context of genomic research, data subjects are granted certain rights under the GDPR. These include, but are not limited to;

"the rights to information (Article 13, 14), data access (Article 15) and portability (Article 20), the rights to erasure (Article 17) and rectification of data (Article 16), rights to object (Article 21) and not be solely a subject to automated data processing (Article 22)." 8 The aforementioned rights are thoroughly discussed in the context of genomic research by Mitchell et al. 9 Transfers of personal data from the EEA/EU to third countries are permitted if the European Commission (EC) has decided that an adequate level of protection is ensured (adequacy decisions). If those are not in place, additional safeguards or a derogatory transfer basis are permissible under the GDPR (Article 45(1), Article 46, Article 49). 10 So far, the Commission has approved "Andorra, Argentina, Canada (only commercial organizations), Faroe Islands, Guernsey, Israel, Isle of Man, Jersey, New Zealand, Switzerland, the UK under the GDPR and the Law Enforcement Directive (LED), 11 Uruguay, South Korea and Japan". Data transfers to these countries are "expressly permitted" without the need to use additional transfer safeguards or a derogatory transfer basis. 12 One such adequacy decision, the US-EU Privacy Shield agreement, was invalidated by the Court of Justice of the European Union (CJEU) in July 2020, 13 which regulated the transfer of personal data from the EU to the US. 14 This agreement allowed the transfer of personal data from the EU to US companies that self-certified pursuant to the Privacy Shield program operated by the US Federal Trade Commission. The overreaching surveillance regime of the US national security apparatus and police enforcement, which allow access to personal data with minimal restrictions and without sufficient redress mechanisms, led to the agreement's invalidation. 15 In addition to invalidating Privacy Shield, the Court further decided that standard contractual clauses (SCCs), a widely used mechanism for transferring genomic and health-related data, must also provide for an "essentially equivalent" level of data protection, viz. the same level of protection mandated by an adequacy decision of the EC.

Data exporters bear the burden of ensuring that the jurisdiction to which they export personal data is capable of offering an essentially equivalent level of data protection pursuant to the SCCs. 16 If not, 6 Borgesius, F. Z., Gray, J., & van Eechoud, M. (2015). Open data, privacy, and fair information principles: Towards a balancing framework. Berkeley Technology Law Journal, 30, 2073-2131. supplementary measures must be undertaken to ensure the protection of data, lest the transfers be suspended outright.

While high-income countries negotiate legal intricacies, LMICs express fears and scepticism of sharing their data in international platforms. Researchers and participants from LMICs worry about exploitation of their efforts, data misuse and inequitable access to benefits arising from research. 17 Indeed, there are already claims of data misuse through commercialisation following data transfers from Africa to a host country. 18 To assuage these fears, researchers advocate measures such as building local capacities in disadvantaged export countries, community engagement in the process of research and data transfer and full transparency within international research endeavours. 19 Furthermore, transferring data from Africa to the EU faces few challenges, but data moving in the opposite direction must confront a number of hurdles due to the EU's stringent standards.

This can be construed as worrisome in collaborative research. 20 A CoC with an international vocation must then contend with these varied concerns regarding international genomic research and transfers of personal data. Individualsboth participants and researchersmust be protected while also giving clear rules for the responsible conduct of genomic and health-related research (Refer to Annex 2).

| MATERIALS (OR SUBJECTS) AND METHODS

To develop this code of conduct we adopted a mixed method approach. It consisted of the following:

1. Determining the gaps in current professional codes of conduct and identifying a need for a CoC within the human genomics fieldthrough a literature review and consulting past SIENNA deliverable reports. 21,22,23,24 2. Synthesizing of some of the most relevant existing professional codes that address biological data and specimen use to develop a preliminary draft of the CoC (See below Annex 1 for exhaustive list of documents). The list was selected via a literature review, and some documents were suggested by SIENNA stakeholders and reviewers.

| The expert panel

A panel of experts composed of 17 members convened online to discuss a proposal for an international code of conduct for data sharing in genomics in September 2020. The purpose of the meeting was to give feedback on the proposed CoC and to identify gaps and concerns. The panel consisted of bioethicists, lawyers, a scientist, medical doctors, a patient organisation representative and a sociologist and two research communicators (see Table 1). In terms of country representation, most of the participants were from Europe, however there were also representatives from Africa, North America and South America.

The members of the panel received a draft of the proposal beforehand. The session was recorded and transcribed by the corresponding author. Follow-up questions were sent to the expert panel via email. Feedback from both the workshop and answers obtained to follow up questions were incorporated into the CoC (Refer to Annex 2).

| SIENNA stakeholders and Scientific Advisory Board

The SIENNA project is one of the European Union's (EU) H2020 research and innovation programme. 25 Its main objective is to address ethical, legal, social and economic aspects of three technology areas, one of which is human genomics and genetics. In addition, the SIENNA consortium produced operational tools that can feed into existing documents to advance ethical research and professional conduct. Part of the SIENNA approach is engaging stakeholders in all different aspects of the analysis and development of operational tools. 26 Consequently, they developed a list of 17 Serwadda, D., Ndebele, P., Grabowski, M. K., Bajunirwe, F., & Wanyenze, R. K. (2018).

Open data sharing and the Global South-Who benefits? Science, 359, 642-643. 18 Moodley, K., & Kleinsmidt, A. (2020). Allegations of misuse of African DNA in the UK: Will data protection legislation in South Africa be sufficient to prevent a recurrence? Developing World Bioethics, 1-6. 19 Retrieved November 10, 2020, from https://sienna-project.eu/ stakeholders with contact information, which partners frequently consulted during the course of the project. 27 Furthermore, a scientific advisory board was assigned to the project and they met regularly with the consortium in order to give feedback and scientific advice during SIENNA research activities and report-writing to the European Commission (EC). 28 The authors solicited input on the proposed CoC via an email sent to a total of 15 stakeholders and 6 responded to our request.

Only those who provided written feedback had their input considered and incorporated.

| FINDINGS AND DISCUSSION

The main purpose of the workshop was to get the experts' views on a preliminary draft CoC as well as to provide some answers to issues raised in the literature regarding international data sharing. Below we discuss those concerns in context of experts' input and the relevant literature.

| Identifiability

Genome sequence data can individuate persons and be linked to other data about them. 29 At present, only 30 single nucleotide polymorphisms are required to identify an individual from their sequence data. 30 Under GDPR, genetic data are defined as a "special category" of data necessitating higher standards of protection and more safeguards (GDPR, Article 9). 31 Similarly, genetic data may also be considered sensitive data in other jurisdictions. 32 To complicate matters further, with the expanding use of big databases whether they are for genomics or healthrelated purposes or both, acquiring "richer" data will be made easier, which would further facilitate identification via crosslinking and extrapolations of various data inputs. 33 Furthermore, huge genetic databases, are created through collaborating research projects, providing to researchers a vast amount of masked or aggregated genetic data during the past few decades. 34 Therefore, pseudonymisation may need to be complemented with further organisational and technical measures that make re-identification more difficult. 35 It would, be wrongful to consider the application of safeguards, such as pseudonymization, on behalf of data custodians, as merely a panacea or a "highway" to data protection. Instead, a dynamic procedure of pre-assessing the de-identification risks and taking into account the current technological developments, should be a prerequisite before genetic data processing takes place. To that extent an international CoC might prove as an indispensable tool, defining specific criteria and requirements that should be applied by the data custodians for the de-identification assessment, especially for those data custodians established in countries where data protection regime is not strong or present at all. These concerns largely echoed those of Philips et al., 36 who brought forward specific concerns regarding identifiability in an international data sharing context. Infringements to privacy rights and interests are sometimes elusive and difficult to account for because of the unclear chain of responsibility, particularly with the use of big transnational databases and cloud computing services.

Moreover, a lack of common terminology regarding identifiability leads to varying degrees of misunderstandings among stakeholders. 37 For instance, in the US de-identification of health data, is perceived as the process of removing 18 specific identifiers, considered to be personally identifiable information, e.g. the data subject's name, telephone number, address, etc. 38 The expert panel questioned the overemphasis on identifiability and protection of privacy within regulations, particularly in the context of genomic data. Some experienced first-hand how this has hindered genomic research. They agreed that sharing of data and transnational transfers are essential to maximally capitalize on genomic research. They further argued that instead of rigid requirements of privacy applied within the research context and the research use of data, regulators and bioethicists should prescribe safeguards against harms that may ensue outside the research context from identifiability and mere breaching of privacy. For example, if the possible harm is authorities accessing sensitive information and using those for law enforcement purposes, including monitoring, profiling or targeting, measures should be in place to prohibit government or law enforcement from illegally accessing such data. A good example is the law that regulates the Estonian Biobank which states specifically that the data held in the biobank may not be used for any other purpose, especially not in civil or criminal proceedings. 39 We note that a CoC cannot provide for robust safeguards in this regard because it is a matter of international and public law.

T A B L E 1 A Panel of
Nevertheless, a CoC can specify that entities subject to it will ensure the validity of access requests by law enforcement, i.e., that the access request is a legal one and not an informal one, and complies with the appropriate formalities.

| Compelled disclosure

One of the problems associated with (international) genomic data sharing is the problem of compelled disclosurei.e. disclosure to public authorities mandated by law. 40 Compelled disclosure is an issue both for domestic and international research. 41 The problem with compelled disclosureas with many nuances regarding privacy rules applicable to cross-border genomic researchis the fragmented nature of rules, even within the EU. The GDPR leaves a number of important aspects, like the rules pertaining to the research use of special categories of data (including genomic and health-related data), and rules regarding compelled disclosure, for the laws of the Member States to regulate. 42 At the same time, the GDPR does not establish which laws should be followed in a cross-border (research) scenario within the EU. 43 The problem of compelled disclosure becomes even more highlighted in the context of data transfers to the US.

As noted, compelled disclosure is a situation when a government authority requests access to personal or health-related data of research subjects/data subjects. In the US, there are special protections provided at the federal and/or the state level against compelled disclosure with regards to divulging research subjects' information. The National Institute of Health can issue a 'certificate of confidentiality', which shields research participants' confidential information given during research. These certificates are applicable on a federal level and on many occasions have safeguarded subjects against courts' demands of such information. However, concerns have been raised regarding conditions for waivers of confidentiality, the extent of the concept of identifiability, ambiguity of some definitions and the scope of these certificates. 44 Furthermore, the certificates can be challenged by the Patriot Act of 2001, which can oblige companies to share data with US authorities. For example, the National Security Agency (NSA) can issue an order for an American cloud company (such as Amazon or Google) to impart data of certain individuals. The injunction has a long reach because it is not confined to US territories only, it extends to data under American companies' "control, custody and possession". 45 Therefore, it can include other geographical areas outside the US. Furthermore, the order is acquired by a secret court and the company for which the order is issued, is forbidden to disclose such a breach to its clients. 46 requiring access to protected data should do so through legal channels, i.e.: contacting national authorities of the data subject to obtain authorization. For an international CoC, broad consent provides an alternative that is viewed practical but still lacks consensus as to its prerequisites. Furthermore, there are challenges to keep data subjects abreast with information regarding new studies that make use of their data. 59 The expert panel reflected on other models of consent as alternatives to individualized informed consent, some experts

| Broad consent

| Return of findings, portability and access

Participants' right to access of genomic data has been debated for some time now. These include discussions of return of uninterpreted data as well as clinically valid and actionable data. While some form of consensus has been reached in the return of incidental findings as to fulfil certain criteria; upon participants' approval (see for example [START_REF] Johansson | Research participants' preferences for receiving genetic risk information: a discrete choice experiment[END_REF] ) and clinically valid and actionable results, [START_REF] Hehir-Kwa | Towards a European consensus for reporting incidental findings during clinical NGS testing[END_REF] the access to "uninterpreted" genetic data is still being debated. However, there are legal as well as ethical foundations that support such access whether in the context of clinical care or genetic research. Some authors have described a few limitations to such rights: risks of serious harm to data subject; to protect confidentiality and privacy and if involves excessive or disproportionate labour. [START_REF] Narayanasamy | Genomic Sequencing Capacity, Data Retention, and Personal Access to Raw Data in Europe[END_REF] An international CoC, according to Phillips et al., should address an optimal way for data subjects to access and transfer their genomic data securely as well as procedures for responsible disclosure of research or health findings. These questions, together with the right of access, return of results and portability were addressed during discussions with the expert panel.

According to the panel, the right to access should incorporate two aspects: access to information about one's data processing and sharing, and right to acquire a copy of one's data. There was a consensus among the experts in response to portability of data query.

For data subjects to make use of their genomic findings, there should be some flexibility to move their data around. They proposed technical tools that would provide secure, user friendly and interoperable file formats and platforms. However, whenever vulnerabilities in security of data exist, transparent communication of the risks of holding a copy of one's own data to users is vital to maintain trust. Some suggested a data protection impact assessment (DPIA) as part of the data management plan to determine the risks of procedures to return results and access of data.

Whereas data access is about "pulling" data at the data subject's request, the return of results is akin to "pushing" data, i.e., at the controller or researcher's determination. The expert panel reflected on the ethical permissibility of returning results as well as its The panel discussed the ways that respecting the withdrawal of consent is reconciled with the interest in continuing the research.

Data protection law may specify, although the data subject's right to withdraw consent is inalienable, the consequences of doing so might not hinder research, as data erasure is not necessarily required and further processing for research purposes might still be possible.

Under the GDPR, the right to be forgotten (i.e. erasure of the data), and the right to object to the further use of the data, can be limited in genomic research when such rights would adversely tamper the research process. 66 The pressing issues that would need to be addressed in an international CoC with respect to withdrawal of consent are: How researchers can fulfil research subjects' withdrawal of data from genomic databases; how aggregate data would be managed and;

creating a system to notify third parties of data withdrawal. 67 Most respondents in the expert panel agreed that aggregate data per se is not personal data and therefore need not be considered for withdrawal of consent. Yet the opinion was to ensure transparency in conveying all the restrictions and prerequisites to all data subjects of when withdrawal of consent can be granted, at the onset of genomic research or the genomic data processing endeavour. For example, stem cell research norms may specify that consent can only be withdrawn up until a certain point, e.g., until the cell lines are in use.

In addition, they expressed that a contact person should be assigned to take charge of protection of such rights, for instance, a Data Protection Officer (DPO). Some members suggested to implement "privacy by design methods" to infrastructures, such as cloud computing, in order to reduce the burden of privacy protection off researchers. To ensure withdrawal of consent and data from third parties, the suggestion was to implement "data mapping" of all third parties involved, extent of their data acquisition, information on onward data transfers etc. Data access committees (DAC) should establish clear and transparent structures and policies that include formal requests to third parties who have accessed/used data, maintaining a catalogue of responses and actions taken by third parties and communicating the results to data subjects.

| Cloud computing

One of the solutions suggested to adopt a uniform 'privacy-bydesign' IT structural approach in genomic research that would lessen the privacy-related burdens on researchers are standardized cloud computing systems. A genomic cloud computing system is defined as "a scalable service where genetic sequence information is stored and processed virtually (i.e., in the 'cloud') usually via networked, largescale data centres accessible remotely through various clients and platforms over the Internet." 68 Researchers can access and handle petabytes (1,000 terabytes) of data from their local office or lab. This is in contrast to a setting that was previously adopted, where research institutes shopped local servers for accessing and processing their data. The technology prides itself as being "locationless", however as noted earlier, unfortunately, privacy laws and regulations are still location-bound and country specific. 69 There are four deployment models for cloud computing 70 namely:

- public clouds, where infrastructure is for the use of the general public hybrid clouds, which combines two or more of the aforementioned models and binds them together using software that permits data and application portability These infrastructures may be supplied by a cloud service provider (CSP), which together with a client agree on Terms of Service (ToS) via either a negotiable contract or a standard non-negotiable one. The common practice is that clients agree to the pre-set contract. 71,72 Among the advantages that cloud computing has over traditional local servers are: relative affordability since it reduces the capital expenditure of research institutes and allows researchers the flexibility to choose services per request from third parties (this function has been called elasticity); global accessibility; reproducibility (since researchers use the same IT tools as provided by cloud service); possible enhanced data security measures; and lastly they are considered environmentally friendly. 73,74 Despite their advantages, cloud computing commercial infrastructures pose certain ethical and legal issues. Dove et al. discuss four major areas of concern namely: data control; data security and confidentiality; data transfer; and lastly accountability. The authors also put forth recommendations to genomic researchers, 75 which we believe are essential to take into account in a code of conduct for international genomic data sharing. To avoid some of these concerns (such as accepting predetermined contracts, risk of compelled disclosure), academic cloud services are growing in number. 76 Scope Europe, a trade association for the information economy, in collaboration with the EC, has published their own GDPRcompliant CoC on cloud computing, which is non obligatory and addresses primarily data processing between businesses, what is referred to "business to business (B2B) cloud computing". 77 In contrast the proposed CoC concerns sharing of genomic data in the context of research (Refer to Annex 2). Processor: "A natural or legal person, public authority, agency or other body which processes personal data on behalf of the controller" (GDPR Art 4 (9)).

List of acronyms/abbreviations

Pseudonymisation: "Means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person" (GDPR Art 4(5)).

| CONCLUSION

Against this rapidly changing background, the genomics community must confront the realities and complexities of legal and ethical norms. For example, recently the EC has issued a new proposal for a regulation titled "the European Health Data Space". 79 The proposal seems to raise more questions than provide answers or the muchanticipated clarity about health data sharing. In fact, the initial reactions are unfavourable. 80 As such, the draft CoC combined with the opinions of the expert panel's members illustrated that high-level concerns revolve around international data transfers, the identifiability of data, access to data by government entities (compelled disclosure), consent, withdrawal of consent, the return of results, and cloud computing. Therefore, the proposed CoC may provide an alternative solution till legal complexities are harmonized.

While complying with the best practices described in this CoC does not guarantee compliance with applicable local norms, it does much to guide genomics researchers in the design and conduct of their research. Local conditions will always be a challenge for a CoC, which must be flexible and sensitive to the possibilities of differences.

Moreover, any CoC must be periodically reviewed to ensure that it remains responsive to the normative and technical environments in which genomics research operates. Finally, it is hoped that this CoC will spur further discussion within the international genomics community about the future of such instruments (Refer to Annex 2). • BBMRI-ERIC for access to and sharing of biological samples and data. [START_REF] Bbmri-Eric | BBMRI-ERIC Policy for Access to and Sharing of Biological Samples and Data Retrieved September 29[END_REF] • International Charter of principles for sharing bio-specimens and data. [START_REF] Mascalzoni | International Charter of principles for sharing bio-specimens and data[END_REF] • WHO's CoC for open and timely sharing of pathogen genetic sequence data during outbreaks of infectious disease. [START_REF]World Health Organization's code of conduct for open and timely sharing of pathogen genetic sequence data during outbreaks of infectious disease[END_REF] • Ethical and legal issues of genomic cloud computing. [START_REF] Dove | Genomic cloud computing: legal and ethical points to consider[END_REF] • GFBR: ethics of data sharing and biobanking in health research. [START_REF] Bull | The ethics of data sharing and biobanking in health research[END_REF] • Framework for responsible sharing of genomic and health-related data. [START_REF] Knoppers | Framework for responsible sharing of genomic and health-related data[END_REF] • The FAIR guiding principles for scientific data management and stewardship. [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF] ANNEX 2: THE CODE OF CONDUCT -A PROPOSAL

• Preamble

The CoC is not a binding legal document but prescribes best practices for big data and cloud computing services. All activities within the SCOPE of this CoC are drafted and intended to be applied in line with the international standards for conducting research such as Declaration of Helsinki and the Universal Declaration of Human Rights. Specific customizations, adaptations and adjustments may be necessary to respect local cultural and ethico-legal norms.

• Scope

The target groups are stakeholders in the process: Researchers, controllers, Data Management Committees (DMC), Data

Access Committees (DAC), cloud service providers and other stakeholders involved in processing of genomic data.

• Purpose

This proposal of a CoC is developed for researchers/ stakeholders who are conducting international genomic research and are utilizing or contributing to big databases which may incorporate cloud computing services.

• A proposal for ethical principles for the CoC

The proposed ethical principles are well established and are featured in many ethical guidelines within the biomedical field. The principles listed below follow a hierarchy, where the first principle takes precedent over the second and the second principle ranks higher than the third one and so forth. They have been arranged

according to their relative importance as judged by the corresponding author. Some of the principles are adopted and/or adapted from the American College of Epidemiology Policy Committee 89 , while others adapted from guidelines of human subject research. 90 -Respect for persons: This is achieved through informed consent procedures and respecting the privacy of data subjects and confidentiality of their data.

-Beneficence and non-maleficence: Research oversight, maximising benefits while minimising risks and transparency of communicating both risks and benefits of data sharing, are measures that fulfil beneficence and non-maleficence.

-Protect and respect for vulnerable populations: For groups and populations that have decreased capacity to give consent and are prone to exploitation, extra protections should be provided to safeguard their rights and wellbeing.

- Transparency should be a principle that is maintained throughout the data cycle and include and not limited to communicating potential risks, respecting privacy and informing of unauthorized access to data.

-Accountability: The data cycle should have a clear chain of responsibility, where stakeholders such as researchers, data processors and controllers, have clearly defined functions and comply to legal and ethical regulations.

-Fair share of benefits and non-exploitation. Whenever data yields benefit in the form of new therapeutics or clinically actionable information, these should be shared fairly with local communities who donated their data when requested.

-Community protection and engagement. This is particularly relevant for data sharing involving minority groups. Whenever possible, local researchers and community leaders should be engaged in research endeavours since they can identify local research needs and best ways to protect data subjects.

-"Attribution: recognizing that the intellectual investment of investigators involved in the creation of data registries is often substantial, and could be acknowledged by mutual agreement". 92 -Data accuracy and minimization: "That the data you transferred are relevant, accurate and limited to what is necessary in relation to the purposes for which it is transferred to and processed". 93 (Figure A1).

• Best practices for (International) data sharing in genomics

A-A governance system Article 1: A data governance system "specifies decision rights and accountabilities for an organization's decision-making about its data. Furthermore, data governance formalizes data policies, standards, and procedures and monitors compliance" 94 (refer to

Diagram 3).

We suggest the formation of a data management committee -Restrict CSP monitoring of stored data.

-Clear safeguards against data breaches, damage or loss of data.

b-Deletion of data and cloud exit strategies -Unambiguous provisions on data deletion, including backups and duplicates, and duration of the grace period (if applicable) and incurred costs for data deletion in the event of CSP contract termination.

-State the agreed grace period where data is kept on the cloud after contract termination.

-Clauses that researchers can retrieve stored and processed genomic data and that a CSP cannot retain it or use it after the contract ends.

-Clear provisions for exit strategies are stated and agreed upon in the event that a cloud service is acquired by a third party.

c-Data privacy and confidentiality -Agreement to treat health and genomic data as "special type of data" that warrants extra security measures and protection of confidentiality.

-Provisions on data encryption, while at rest and as it is being transferred.

3.

  Involving stakeholders in the process of refining the proposed draft, an expert panel meeting was held on September 10, 2020, to discuss the proposed CoC and obtain input on gaps as identified in the literature. 4. The method was reiterative, involving other stakeholders as well: a) SIENNA scientific advisory board and b) SIENNA's stakeholder list (via email contact) to review and suggest further amendments to the proposed CoC. 5. Some members of the expert panel provided further comments and suggestions for the CoC.

  25SIENNA Communication Team. (2018). SIENNA: Technology, ethics and human rights.

A

  broad consent refers to a consent form in which research participants can agree to future and unspecified research with ongoing governance. The exact breadth of consent depends on circumstances, with participants typically consenting to areas of scientific research. Generally, a broad consent includes delegating some of the decision making to an independent oversight body, usually a research ethics committee and/or data access committee and the commitment to ongoing future communication with research participants or donors of biospecimens.[START_REF] Grady | Broad consent for research with biological samples: workshop conclusions[END_REF] However, donors or research participants have indicated their unwillingness to the use of their samples in certain kinds of research, e.g., research undertaken by for-profit companies, research on indigenous populations, and human cloning.[START_REF] Brothers | Two large-scale surveys on community attitudes toward an opt-out biobank[END_REF][START_REF] Gaskell | Publics and biobanks: Pan-European diversity and the challenge of responsible innovation[END_REF][START_REF] Mccarty | Community consultation and communication for a population-based DNA biobank: The Marshfield clinic personalized medicine research project[END_REF][START_REF] Stegmayr | Informed consent for genetic research on blood stored for more than a decade: a population based study[END_REF][START_REF] Tupasela | Attitudes towards biomedical use of tissue sample collections, consent, and biobanks among Finns[END_REF] Furthermore, it has been shown that research participants are not so much concerned with the specific purpose of a planned project but more concerned about the protection of their personal data against unauthorized use, i.e., illegitimate use by non-researchers.[START_REF] Hoeyer | Denmark at a crossroad? Intensified data sourcing in a research radical country[END_REF] Broad consent may imply a broad description of the purpose of research, e.g., cancer research or research on rare diseases, but involve a more specific description regarding the processes of research, e.g., collaboration with sharing of data across national borders, collaboration with commercial entities or use of genomic analyses.

  mentioned familial consent in the context of genomic data while others discussed broad consent. The former is a type of consent that has been used in the context of rare disease research where results were returned to family members of research participants. 60 Using familial consent in genomic research would warrant consideration of the cultural context in which participants are located. Further sociological research could address cultural nuances of family, sensitive information and data sharing. With regards to broad consent, the panel reiterated what the literature discussed in terms of delineating requirements for a broad consent and creating processes to support participants' input and preferences.

  feasibility. In response to what constitutes "responsible communication" of these findings, the members in expert panel stated; firstly, a data subject/patient has indicated their preference to receive findings; secondly a decision is made of whether the findings possess clinical validity, utility and actionability; thirdly ensuring communication of good quality data (accurate and reliable); and lastly, advice on professional support e.g., a genetic counsellor. A distinction was made between patients and volunteers in regards to their expectations. Patients might expect to receive relevant findings but volunteers may not.4.5 | Withdrawal of consentConventionally, withdrawal of consent has been an essential pillar in human subject research and was clearly stated in informed consent documents as a valid choice for research participants.64 However, guaranteeing withdrawal of consent becomes tricky in genomic therapies and diagnostics, since the genetic sequence of patients or volunteers can be incorporated in these new treatments or investigative tools. An infamous example of a cell line being generated and widely disseminated without proper consent (and thus no possibility of withdrawal) concerns the HeLa cell line.65

  and multinational companies with a particular focus on healthcare and telecommunications. Adam has also worked at highly ranked universities as a tutor and legal researcher, published books on biobanks and bioethics, and contributed to papers on the regulation of emerging technologies and digital ethics. This paper is associated with his former employment at Trilateral Research Ltd. Gauthier Chassang is a lawyer working at Inserm since 2010. He is specialised in EU law, (bio)ethics, human rights, ICTs in health, AI, biobanking, governance and privacy protection applied to health research activities. Based at the University of Toulouse Faculty of Medicine, he actively participates in expertise and counselling services at Inserm and in the context of the Genotoul Societal Platform "Ethique et biosciences", as well as in several EU and international research projects. Olga Tzortzatou is an Attorney at Law, qualified to stand before the Court of Appeals. Her field of expertise is privacy law, intellectual property law and bioethics. She also serves as an external ethics expert for the European Commission. 44444Kärt Pormeister, PhD, is the legal counsel of the Estonian Biobank, which is part of the Institute of Genomics of the University of Tartu, and holds the genetic and health data of more than 200k Estonians. Kärt obtained an LLM in health law as a Fulbright scholar from the University of Houston in Texas, USA. In her PhD studies at the University of Tartu, Kärt focused on transparency in relation to data subjects in genetic research. Elias Uhlin, PhD, defended his PhD from the Karolinska Institute with a thesis titled "iPS Cells In Regenerative Medicine" and followed it up with a post doc investigating a cryopreservable off the shelf cell therapy for spinal cord injury. He recently concluded his work as a lead scientist for the CDMO CCRM in Toronto, Canada, and is currently active at Lund University within the Indicell network focused on building a pre-GMP facility infrastructure to accelerate ATMP development. Antonella Cardone, MSc, MBA, is the Cancer Patient ), of which she was Vice-Chair. She was the Director of the European Cancer Patient Coalition (www. ecpc.org) and she sat on the Board of All.Can (https://www.allcan.org/). She holds a Master's in Science and one in Business Administration (MBA). Michael J. S. Beauvais is a doctoral candidate at the University of Toronto's Faculty of Law. His doctoral research on children's privacy from their parents brings together thinking on privacy, children's agency, parental authority, and informationcommunication technologies. He also writes about data protection law and its interaction with health-related research. How to cite this article: Matar, A., Hansson, M., Slokenberga, S., Panagiotopoulos, A., Chassang, G., Tzortzatou, O., Pormeister, K., Uhlin, E., Cardone, A., & Beauvais, M. (2022). A proposal for an international Code of Conduct for data sharing in genomics. Developing World Bioethics, 1-14. https://doi.org/10.1111/dewb.12381 ANNEX 1 LIST OF DOCUMENTS, ETHICS AND PROFESSIONAL CODES THAT HAVE BEEN SYNTHESIZED TO DRAFT THE COC • CoC for user access to the RD-Connect Genome-PhenomeAnalysis Platform (GPAP) for health-related information.81

  "Freedom of scientific enquiry: custodianship (stewardship) should encourage openness of scientific enquiry, and should maximize data use and sharing to the extent that it promotes health." 91 -Transparency: Researchers should communicate transparently their motivations and research plans with relevant stakeholders.

(

  DMC) that establishes a governance system, which among other things, would oversee relationships with cloud service providers and review requests of transfer and share of data by researchers or research teams. For every project where data sharing is necessary or proposed, a request is reviewed by the DMC with the ultimate goal to pursue the FAIR principles (findability, accessibility, interoperability and reusability) of data use.95 The DMC would have a Data Protection Officer (DPO), at least a patient group representative, a member from the research team, IT security personal, a bioinformatician, a bioethicist, a clinician and a public representative. The DMC would ensure the compliance to a robust governance system and guide those who are responsible to: a-Establish Terms of reference (ToR) for use of cloud computing or big databases which include: Clear definitions of terminology, defined roles of stakeholders and harmonize terminology used among the stakeholders. Each defined role or function within the 89 Ness, R. B. (2007

-

  data cycle should include extent of data access, processing and sharing.b-Defining a chain of reporting and responsibility among stakeholders (users, processors of data, controllers, funders, cloud computing services providers, etc.) in terms of data import and export, processing, access, return of data-processed findings and security. c-Establish an error management system and related standard operating procedures to report incidents of data breach or hacking or misuse or unauthorized access by law enforcement and third parties. d-Establish auditing systems both internally and externally to ensure compliance to ToR. e-Lay down measures to settle conflicts that arise between different stakeholders. f-Set procedures to ban or penalize non-compliant partners from using the database. g-Define research goals and direction h-Negotiate contracts with commercial cloud service providers, transfer and share of data upon requests by researchers or research teams. A-Data collection Article 2: Researcher should ensure that data subjects (patients, volunteers and/or research participants) receive sufficient information as defined by the local legal regime. Informed consent offered should depend on the nature and type of research, whether a protocol which can be disease/context/sub-population specific or very broad i.e., creating a data resource/biobank for use by others. The data subject should obtain the following information: a-Data controller/s information and contact details. b-Voluntary nature of data or sample donation. c-The purposes (specific or broad according to the nature of research) of the processing including possibility of secondary use of data, if needed. d-Potential risks to identifiability of processed data. e-The likelihood of transnational data transfers as well as secondary transfer. f-If complementary information will be added through linkage to different data registries, medical records, etc. g-Methods of funding whether academic or mixed model funding with commercial entities. h-The process for reporting of research results and incidental findings (refer to Article 7 for more details). i-That consent may be withdrawn and how this is done." (refer to Article 5 for more details (adapted from RD-connect, 2018) 96 ). j-Reconsent procedures from children and new-borns who have had their genomic data collected, when they come of legal age. Also, participants who are engaged in long term cohorts should reconsent periodically (for example every 10-15 years). k-How records of consent, reconsent are maintained and kept up to date. Article 3: Any consent form and process should comply to international ethical requirements and should be approved by the local research ethics committee. Other models of consent should be adapted and adopted as alternatives to accommodate cultural and social differences as well as nature of research (see Article 2 above). Article 4: To fulfil broad consent requirements: a-Researchers should develop tools (a platform with a notification alert to new projects whenever a donor sample is used) for keeping donors/research participants informed about the types of research being conducted on their data. b-Researchers should conduct periodic re-assessment of attitudes and research preferences of the data subjects. c-Researchers should be aware that broad consent can differ across geographical areas since research should have a social value for the targeted community. d-Data collectors/researchers should record and report patients' or data subjects' preferences (e.g.: to participate in only certain types of research) to their data use. This would warrant extra resources and an ongoing communication with data subjects. Article 5: Researchers should enable the option of withdrawal of consent to data use in researchwhich is limited by: a-If it is practically impossible to withdraw the data (data has been used to manufacture therapies or diagnosticse.g.: cell cultures, stem cell therapies). b-If it would critically impact the purpose to which data was processed. c-If the data has been aggregated. In situations where withdrawal of consent is set up, researchers shall create the appropriate channels of communications and F I G U R E A1 The governance system for data-sharing 96 Hansson, et al., op. cit. note 1. reporting to data subjects, third parties, and data processors to ensure withdrawal of data from all partners. Article 6: Researchers should ensure the quality of the data used in their research projects by complying to international standards. Article 7: Researchers should return genomic data findings to data subjects, while explaining the risks that are associated with such reidentification. The measures should be appropriated to local healthcare and legal systems. To return the findings researchers should: a-Return findings to data subjects who indicated their interest to receive findings and in keeping with local healthcare and legal systems. b-Return findings that are clinically valid, actionable and have been confirmed by a clinical laboratory. c-Recommend that data subjects seek professional advice such as a genetic counsellor or their General Practitioner. d-If clearly stated in the research protocol and research participants request uninterpreted data for diagnostic purposes, researchers might provide secure, user-friendly and interoperable platforms i.e., presented in a language and a format that can be machine and human readable. 97 B-Data storage Article 8: Data collected can be stored in local servers, international and/or cloud computing services (commercial, academic or public). Whatever the platform used, researchers should ensure security and safety of data are maintained. a-Cloud computing storageFor the purpose of this CoC, the focus will be on best practices of cloud computing storage. The procedures have been adopted from.98 Article 9: Research institutions should as much as possible opt for academic cloud services, or cloud services that can abide by SCCs, whenever possible and feasible. Article 10: When using commercial cloud services, research institutions/data management committees should arrange for negotiable contracts with commercial CSPs and avoid standard predetermined contracts. Article 11: To negotiate Terms of Service (ToS) with CSP, data management committees/research institutes should have, Amendments to ToS: Clear procedures for proper notification of amendments in ToS including allocating adequate time for response and acceptance by cloud users. a-Support and maintenance -Clear instructions on upholding confidentiality of genomic data, while CSPs conduct their support activities such as maintenance of cloud computing service.

Article 12 :

 12 Researchers should limit authorizing access to cloud services or genomic database to only members of the research team conducting research (e.g.: technicians, bioinformaticians, research assistants) who have been approved by a DMC or a DAC. Article 13: Researchers should only deposit encrypted data for storage in a database or a cloud computing service and ensure there are regular back up of the data. For traceability, all submitted datasets are linked to the username of the data submitter within the system. 99 Article 14: In cases of breaches to data safety (e.g.: data losses or damage) or security (unauthorized access), the CSP should notify researchers within a brief specified period. C-Data sharing, transfer and access Article 15: To share or transfer or exchange data, researchers must do so by using minimal data for the purpose, encrypting genomic data and only using secure channels that are protected against unauthorized access. Article 16: Researchers should transfer data that are relevant and limited to what is necessary in relation to the purposes for which it is transferred and processed. Article 17: Researchers should maintain records of data transferral, users' activity and logging to the database or cloud service for auditing purposes and accountability. 100 97 Wilkinson, et al., op. cit. note 1. 98 Dove, et al., op. cit. note 5. 99 Hansson, et al., op. cit. note 1.

  Serwadda, et al., op. cit. note 17.

	20 Slokenberga, S., Reichel, J., Niringiye, R., Croxton, T., Swanepoel, C., & Okal, J. (2019). EU
	data transfer rules and African legal realities: is data exchange for biobank research realistic?
	International Data Privacy Law, 9, 30-48.
	21 Soulier, A., Niemiec, E., & Howard, H. C. (2019). SIENNA D2.4: Ethical Analysis of Human
	Genetics and Genomics. Retrieved September 22, 2022, from https://zenodo.org/record/
	4068016#.Yyyqb1zMLIU
	22 Howard, H. C., Niemiec, E., & Soulier, A. (2019). SIENNA D2.1: State of the art review of
	human genomic technologies. Retrieved September 22, 2022, from https://zenodo.org/
	record/4067912#.Yyyqn1zMLIU
	23 Howard, H., Niemiec, E., Tambornino, L., & Lanzerath, D. (2019). SIENNA D2.3: Survey of
	REC approaches and codes for genomics. Retrieved September 22, 2022, from https://
	zenodo.org/record/4066865#.YyyrP1zMLIU
	24 Siemaszko, K., Rodrigues, R., & Slokenberga, S. (2020). SIENNA D5.6: Recommendations
	for the Enhancement of the Existing Legal Frameworks for Genomics, Human Enhancement,
	and AI and Robotics. Retrieved September 22, 2022, from https://zenodo.org/record/
	4121082#.YyyrrFzMLIU

  Experts by Profession

	Profession	Number
	Bioethicists	5
	Lawyers	5
	Bio-scientist	1
	Medical doctors	2
	A patient organization representative	1
	Sociology and medical humanities	1
	Research/science communication professional	2
	Total	17

27 Rodrigues, R., & Broadhead, S. (2018). D1.2: Stakeholder Analysis and Contact List. Retrieved September 22, 2022, from https://www.sienna-project.eu 28 Rodrigues & Broadhead, op. cit. note 26. 29 Rodriguez, L. L., Brooks, L. D., Greenberg, J. H., & Green, E. D. (2013). The complexities of genomic identifiability. Science, 339, 275-276. 30 Dankar, F. K., Ptitsyn, A., & Dankar, S. K. (2018). The development of large-scale deidentified biomedical databases in the age of genomics-principles and challenges. Human Genomics, 12, 19. 31 European Parliament and Council of European Union, op. cit. note 7. 32 Mitchell, et al., op. cit. note 5. 33 Ibid: 1-17. 34 Bovenberg, J., Peloquin, D., Bierer, B., Barnes, M., & Knoppers, B. M. (2020). How to fix the GDPR's frustration of global biomedical research. Science, 370, 40-42. 35 Mitchell, et al., op. cit. note 5. 36 Phillips, et. al., op. cit. note 2. 37 Laurie, G. T., & Coleman, C. H. (2020). Biobanks. In The Oxford Handbook of Comparative Health Law.

  Similarly, the EU does not protect against such transgressions either. However, national regulations of EU member states may provide such protections which are made possible under GDPR Art 23 but requires much justification.

47

Bennett et al. proposed tools to overcome or reconcile these conflicting legal intricacies. Some of them have been invalidated in the EU such as the safe harbour in October 2015

48

and the Privacy Shield agreement in July 2020 49 , while others need further corroboration that they actually work. They include:

-Standard Contractual Clauses (SCCs) -Binding Corporate Rules.

The European Data Protection Board (EDPB) proposed the SCCs as a legal method to limit data access requests by local authorities.

These SCCs would mandate that companies use technical processes such as "encryption, pseudo-anonymisation or split processing of data".

50

Data importers are compelled to report data access requests (or actual data breaches) by local authorities or failure to fulfil any related contractual agreements to data exporters. Moreover, data importers should provide legal support and means of challenging public authorities' data access requests.

51

According the EDPB, split processing of data consists of sending chunks of data to different processors to reduce the chances that the data that were sent to each individual processor being personal data.

In an answer to compelled disclosure, the expert panel had several suggestions; some of which were legal while others functional. While the former reflected many of the recommendations of the EDPB, one expert suggested that foreign legal authorities

39

Pormeister, K. (2018). Genetic research and applicable law: the intra-EU conflict of laws as a regulatory challenge to cross-border genetic research. Journal of Law and the Biosciences, 5, 706-723.

40

Phillips, et al., op. cit. note 2. 41 Pormeister, op. cit. note 39. 42 Tzortzatou, O., Slokenberga, S., Reichel, J., da Costa Andrade, A., Barbosa, C., Bekaert, S., van Veen, E.-B., et al. (2021). Biobanking Across Europe Post-GDPR: A Deliberately Fragmented Landscape. In S. Slokenberga, O. Tzortzatou, & J. Reichel (Eds.), GDPR and Biobanking. Law, Governance and Technology Series, Vol 43. Springer, Cham. 43 Pormeister, op. cit. note 39. 44 Bennett, et al., op. cit. note 15. 45 Ibid. 46 Ibid. 47 European Data Protection Board. (2021). Guidelines 10/2020 on Restrictions under Article 23 GDPR (Version 2.0). Brussels: Available at https://edpb.europa.eu/our-work-tools/ourdocuments/guidelines/guidelines-102020-restrictions-under-article-23-gdpr_en 48 Monteleone, S., & Puccio, L. (2017). From safe harbour to privacy shield: advances and shortcomings of the new EU-US data transfer rules. European Parliamentary Research Service (EPRS), 1-39.

Rodrigues, R., & Broadhead, S. (2018). D1. 1: The Consortium's Methodological Handbook. Retrieved September 22, 2022, from https://www.sienna-project.eu

Jacques, op. cit. note 13. 50 EDPB. (2020). EDPB recommendations, supplementary measures transfers tools ensure compliance with the EU level protection of personal data. 51 Ibid.

14718847, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/dewb.12381 by INP Toulouse, Wiley Online Library on [03/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

Dove, E. S., Joly, Y., Tassé, A.-M., & Knoppers, B. M. (2015). Genomic cloud computing: legal and ethical points to consider. European Journal of Human Genetics, 23, 1271-1278.

Langmead & Nellore, op. cit. note 68.

Ibid.

ACKNOWLEDGEMENTS

This article has been adapted from/is based upon the Deliverable 5.2 report of the SIENNA project (Stakeholder-informed ethics for new technologies with high socio-economic and human rights impact)which has received funding under the European Union's H2020 research and innovation programme.

We would like to extend our thanks to Manuel Guerrero, Konrad Siemaszko and Stefan Eriksson for their insightful comments and review of the manuscript. We are also grateful for SIENNA's scientific advisory board and expert stakeholders for their valuable advice and input on the CoC particularly, we name: Prof. Roger Brownsword, Prof. Benjamin Caps, Prof. Bartha M. Knoppers, Prof. Christine Patch, Prof. Francesca Forzano, and Dr. Mark Coeckelbergh.

Horizon 2020 Framework Programme, Grant/Award Number: 741716

FUNDING

Funded by SIENNA project, which has received funding under the European Union's H2020 research and innovation programme under grant agreement No 741716.

CONFLICTS OF INTEREST

Authors declare no conflict of interests.

DISCLAIMER

This article and its contents reflect only the views of the authors and does not intend to reflect those of the European Commission.


MAC Protocol for Cooperative Networks with Efficient Selection of Relay Terminals Benoît Escrig

Université de Toulouse Email: escrig@enseeiht.fr Abstract-We propose a new cooperative Medium Access Control (MAC) protocol in the context of fixed wireless networks. The protocol implements on-demand cooperation and a selection of the best relay. Hence, cooperation between a source terminal and a destination terminal is activated only when needed. In that case, only the best relay among a set of available terminals is retransmitting the source message to the destination terminal. We improve this typical approach using three additional features. First, we use a splitting algorithm to select the best relay. This ensures a fast selection process. Moreover, the duration of the selection process is now upper bounded by a known value. Second, only terminals that can improve the direct link are allowed to participate to the relay selection. By this mean, inefficient cooperation is now avoided. Finally, the destination terminal discards the source message when it fails to decode it. This saves processing time at the relay candidates. We prove that the proposed protocol achieves an optimal performance in terms of Diversity-Multiplexing Trade-off (DMT).

I. INTRODUCTION

Cooperative communications provide an interesting alternative to Multiple Input Multiple Output (MIMO) techniques when wireless systems cannot support multiple antenna terminals [START_REF] Laneman | Energy-efficient antenna sharing and relaying for wireless networks[END_REF]- [START_REF] Hunter | Diversity through coded cooperation[END_REF]. In a cooperative scenario, a source terminal S sends data to a destination terminal D through a direct path. One or several relay terminals help the transmission by receiving the source message and forwarding it to D through a relaying path (see Figure 1). Hence the direct path is rendered more robust. However, this comes at the price of bandwidth consumption so that the system operates at diminished capacity 1 .

One common way to compare cooperative transmission techniques is to compute the Diversity-Multiplexing Tradeoff (DMT) [START_REF] Zheng | Diversity and multiplexing: A fundamental tradeoff in multiple antenna channels[END_REF]. The DMT analysis of a transmission scheme yields the diversity gain d(r) achievable for a spatial multiplexing gain r. The diversity gain helps in quantifying the robustness of the S-D link and the multiplexing gain gives an hint on the capacity of the link. Both indicators should be maximized in order to get an optimal DMT curve. When (m -1) relay candidates are involved in a cooperative scenario, the optimal DMT curve, d(r) = m(1 -r) for 0 ≤ r ≤ 1, is achievable by protocols that implement both on-demand relaying and a selection of the best relay [START_REF] Bletsas | Opportunistic cooperative diversity with feedback and cheap radios[END_REF], [START_REF] Escrig | On-Demand Cooperation MAC Protocols with Optimal Diversity-Multiplexing Tradeoff[END_REF]. In an on-demand relaying scenario [START_REF] Laneman | Cooperative diversity in wireless networks: efficient protocols and outage behavior[END_REF], [START_REF] Gomez | Cooperation on demand protocols for wireless networks[END_REF], the relay terminal is transmitting only when D fails in decoding the data transmitted by S. This approach allows maximization of the spatial multiplexing gain. Moreover, when cooperation is needed, only the best relay terminal retransmits the source message [START_REF] Bletsas | A simple cooperative diversity method based on network path selection[END_REF]. This allows maximization of the diversity order. Hence, the optimal DMT curve is achieved.

Two limitations arise when implementing cooperative protocols with the two features mentioned above. First, one relay may be chosen even if it cannot improve the direct path. Second, the selection of the best relay is not collision free so that it is not possible to predict the amount of time required to carry out this task. The first and the second issues have been addressed in [START_REF] Chou | Cooperative mac protocol with automatic relay selection in distributed wireless networks[END_REF] and in [START_REF] Azgin | Cooperative mac and routing protocols for wireless ad hoc networks[END_REF], [START_REF] Liu | Coopmac: A cooperative mac for wireless lans[END_REF] respectively. However, the proposed protocols have not been designed with the purpose of optimizing both the DMT of the transmission scheme and the signaling needed to select the relays.

To tackle these issues, we improve the cooperative MAC (Medium Access Control) protocols in [START_REF] Bletsas | Opportunistic cooperative diversity with feedback and cheap radios[END_REF], [START_REF] Escrig | On-Demand Cooperation MAC Protocols with Optimal Diversity-Multiplexing Tradeoff[END_REF] with the following additional features:

• splitting algorithm for fast relay selection: splitting algorithm can find the best relay terminal, on average, within at most 2.507 slots even for an infinite number of relay candidates [START_REF] Qin | Opportunistic splitting algorithms for wireless networks[END_REF], [START_REF] Shah | Splitting algorithms for fast relay selection: Generalizations, analysis, and a unified view[END_REF]. Collision between relay candidates are not avoided but the contention time is now upper bounded.

• pre-selection of the relay terminals: the efficiency of the selection process is guaranteed by pre-selecting only terminals that are able to improve the direct transmission. Inefficient cooperation is now avoided. • source message dropping: the destination terminal discards the source message when it fails to decode it. This saves processing time without sacrificing the optimality of the DMT. We show that this on-demand relaying protocol with selection of the best relay terminal provides an optimal performance in terms of DMT. This cooperative protocol has been designed in the context of IEEE 802.11-based mesh networks. Though restricted to this standard in this paper, we believe that our proposal can also be applied to other wireless systems such as wireless sensor networks, broadband wireless networks, and broadcast wireless systems. In section II, the protocol is described in details. Section III presents the DMT analysis of the protocol. In particular, we show the optimality of the DMT when the relaying scheme is based on a selective Decode-and-Forward (DF) method. We conclude in section IV.

II. ON-DEMAND RELAYING WITH SELECTION OF THE BEST RELAY TERMINAL A. System model

We consider a slow Rayleigh fading channel model in which delay constraints are on the order of the channel coherence time. Each relay terminal cannot transmit and listen simultaneously (half duplex constraint). Moreover, transmissions are multiplexed in time, they use the same frequency band. The channel gain h ij between a transmitting terminal i and a receiving terminal j, is assumed to be accurately measured by the receiver j, but not known to the transmitter i. We also assume that the channel gain h ij is identical to the channel gain h ji . This assumption is relevant since both channels are using the same frequency band. Statistically, channel gains h ij are modeled as i.i.d circularly symmetric complex Gaussian random variables with zero mean and equal variance σ2 . Let P be the power transmitted by each terminal and σ 2 w be the variance of the AWGN (Additive White Gaussian Noise) in the wireless channel. We define SN R = P/σ 2 w to be the effective signal-to-noise ratio.

We also restrict our study to a single source-destination pair. It is assumed that (m-1) terminals are located in the range of both the source and the destination terminals. These terminals are available for implementing a cooperative transmission and they are not allocated to any other transmission. However, these (m -1) terminals are likely to cause collision if they try to transmit data all at once. All other terminals are assumed to remain silent because they do not implement a cooperation functionality, or their cooperation functionality has been switched off. Hence, no extra interference occurs from neighboring terminals. In any case, if a terminal should interfere with the cooperative transmission, the proposed protocol is implementing classical error recovery mechanisms.

B. Protocol Description 1) Cooperation Mode Activation:

The cooperation mode is activated at terminal R i , 1 ≤ i ≤ (m -1), upon reception of a data frame from any source terminal S. This triggers the relay selection process at the relay candidates. The data frame is stored when R i is implementing the cooperation functionality and R i is not already involved in any other transmission. When terminal D succeeds in decoding the data frame, it sends an acknowledgment frame (ACK). Otherwise, terminal D discards the data frame and sends a signaling frame (CFC for Claim For Cooperation) [START_REF] Gomez | Cooperation on demand protocols for wireless networks[END_REF]. This saves processing time without sacrificing the optimality of the DMT. Note that the data frame from S contains an additional control field on the source address. Hence, when the checksum on the entire frame is wrong and the checksum on the source address is good, the destination terminal is able to send a CFC with the source address. When the CFC frame is lost, the protocol implements classical error recovery mechanisms 2 . When a terminal R i stores the source message, it waits for either an ACK frame or a CFC frame. If any of these two frames is not received within a given time-slot, the source message is discarded at terminal R i . Hence, only terminals that have received both the data frame and the CFC frame trigger the relay selection process 3 .

2) Splitting algorithm: Consider a time-slotted system with (m -1) relay candidates. Each terminal R i has a suitability metric u i , defined as the mutual information of the cooperative transmission from S to D, through terminal R i . The goal is to select the terminal with the highest metric. The metrics are continuous and i.i.d. with complementary CDF (CCDF) denoted by F c (u) = Pr[u i > u]. Therefore, the F c (.) is monotonically decreasing and invertible. The algorithm is specified using three variables H L (k), H M (k), and H min (k) following [START_REF] Qin | Opportunistic splitting algorithms for wireless networks[END_REF]. H L (k) and H M (k) are the lower and upper metric thresholds, respectively, such that a terminal R i transmits at time slot k if and only if its metric u i satisfies H L (k) < u i < H M (k). H min (k) tracks the largest value of the metric known up to slot k above which the best metric surely lies.

Initialization: In the first slot (k = 1), the parameters are initialized as follows:

H L (1) = F -1 c (1/N r ), H H (1)
= ∞, and H min (1) = 0. The parameter N r denotes the number of possible relays and should be set to (m-1). So, each terminal should know the value of N r . When this value is not known at each terminal, it can be overestimated by the number of terminals in the range of D. Terminal D generally knows this number through upper layer protocols. This overestimation has no impact on the latency induced by the splitting algorithm [START_REF] Shah | Splitting algorithms for fast relay selection: Generalizations, analysis, and a unified view[END_REF].

Transmission rule: At the beginning of each slot, each terminal locally decides to transmit if and only if its metric lies between H L (k) and H H (k).

Feedback generation: At the end of each slot, the destination terminal broadcasts to all terminals a two-bit feedback: (i) 0 if the slot was idle (when no terminal transmitted), (ii) 1 if the outcome was a success (when exactly one terminal transmitted), or (iii) e if the outcome was a collision (when at least two terminals transmitted).

Response to feedback:

Let split(a, b) = F -1 c ( Fc(a)+Fc(b)
2 ) be the split function. Then, depending on the feedback, the following possibilities occur:

• If the feedback (of the kth slot) is an idle (0) and no collision has occurred so far, then set

H H (k + 1) = H L (k), H L (k + 1) = F -1 c ( k+1 Nr ) and H min (k + 1) = 0. • If the feedback is a collision (e), then set H L (k + 1) = split(H L (k), H H (k)), H H (k + 1) = H H (k), and
H min (k + 1) = H L (k).
• If the feedback is an idle (0) and a collision has occurred in the past, then set

H H (k + 1) = H L (k), H L (k + 1) = split(H min (k), H L (k)), H min (k + 1) = H min (k).
Termination: The algorithm terminates when the outcome is a success [START_REF] Laneman | Energy-efficient antenna sharing and relaying for wireless networks[END_REF].

3) Data transmission: When the destination terminal sends its last feedback, the best relay terminal sends a copy of the data frame using a selective DF forwarding scheme. The destination receives the signal from the best relay terminal. When D succeeds in decoding the data frame, D sends an ACK frame (see Figure 2). Otherwise, D remains silent and the timeout at the source terminal triggers a re-transmission. Fig. 2. Frame exchange sequence in the protocol using the basic IEEE 802.11 access method (S is the source terminal, D is the destination terminal, B is the best relay terminal, and R i is a relay candidate).

4) Remarks:

We give here some additional comments on the protocol design:

• RTS/CTS optional access method: several cooperative MAC protocols rely on the exchange of modified Request-to-Send (RTS) and Clear-to-Send (CTS) signaling frames [START_REF] Chou | Cooperative mac protocol with automatic relay selection in distributed wireless networks[END_REF]- [START_REF] Liu | Coopmac: A cooperative mac for wireless lans[END_REF]. If CTS frames transmitted by the destination terminal D can be modified, we can infer that channel state information is available at the transmitter. Hence, the source can actually choose not to transmit when a target capacity cannot be supported. This gives rise to new cooperative protocols, the study of which is left for future work. • NAV modification: the Network Allocation Vector (NAV) values at each terminal should be increased according to the new frame scheduling. This should avoid unnecessary soundings by neighboring terminals. • Error recovery mechanism: as soon as a frame is missing, when a collision occurs, or when the set of efficient relays is empty, the source message is discarded at the relay terminals and the source terminal triggers a retransmission after a given timeout.

III. DMT ANALYSIS OF THE ON-DEMAND COOPERATIVE PROTOCOL

In this section, we study the DMT curve of the proposed protocol. We characterize our channel models using the system model described in the previous section, and a time-division notation; frequency-division counterparts to this model are straightforward. We use a base-band-equivalent, discrete-time channel model for the continuous-time channel. Three discrete time received signals are defined in the following. Here, y ij (n) denotes the signal received by terminal j and transmitted by terminal i. During a first time-slot, D and the best relay terminal B are receiving signals from S

y SD (n) = h SD x(n) + w SD (n) (1)
y SB (n) = h SB x(n) + w SB (n) (2)
for n = 1, 2, ..., T M /2, where T M denotes the duration of time-slots reserved for each message. When terminal D succeeds in decoding the data frame from S, no signal is transmitted by the best relay terminal B. Otherwise, the best relay terminal sends a new signal using a selective DF scheme, i.e. if and only if it has been able to decode the source message. So we consider that the estimation of signal x(n), denoted x(n), is error free. Hence, during the second time slot, D is receiving a signal from B

y BD (n) = h BD x(n) + w BD (n), if I SB > R 0, if I SB ≤ R
for n = T M /2 + 1, ..., T M , where the mutual information I SB is given by

I SB = log 2 (1 + SN R|h SB | 2 ) ( 3
)
The noise w ij (n) between transmitting terminal i and receiving terminal j are all assumed to be i.i.d. circularly symmetric complex Gaussian with zero mean and variance σ 2 w . Symbols transmitted by the source terminal S are denoted x(n). For simplicity, we impose the same power constraint at both the source and the relay: E[|x(n)| 2 ] ≤ P . We assume that the source and the relay each transmit orthogonally on half of the time-slots. We also consider that a perfect synchronization is provided at the block, carrier, and symbol level.

We define the diversity gain d(r) of the protocol by

d(r) = lim SN R→∞ - log[p out (SN R, r)] log(SN R)
The probability p out (SN R, r) is the outage probability for a signal to noise ratio SN R and a spatial multiplexing gain r defined by

r = lim SN R→∞ R log 2 (SN R)
where R is the spectral efficiency of the transmission (in b/s/Hz). For high SN R values, we use

R = rlog 2 (SN R)
As in [START_REF] Laneman | Distributed spacetime-coded protocols for exploiting cooperative diversity in wireless networks[END_REF] and [START_REF] Bletsas | A simple cooperative diversity method based on network path selection[END_REF], the event that the relay has successfully decoded the data transmitted by S with a spectral efficiency R is equivalent to the event that the mutual information of the channel between S and the best relay B, I SB , lies above the spectral efficiency R. Assuming that (m -1) terminals are available, the protocol is in outage if all the relay terminals fail in improving the direct transmission

p out (SN R, r) = Pr[I D ≤ R] × Pr[ m-1 i=1 (I (i) ≤ R 2 )|I D ≤ R] (4)
where

I (i)
is the mutual information of the relayed transmission using selective DF cooperation scheme at terminal R i and implementing frame dropping at the destination terminal (the source message is discarded at the destination terminal when cooperation is needed)

I (i) = 1 2 log 2 (1 + SN R|h SD | 2 ), if I SRi ≤ R 1 2 log 2 (1 + SN R|h RiD | 2 ), if I SRi > R (5)
where the mutual information I SRi is defined by

I SRi = log 2 (1 + SN R|h SRi | 2 )
and the mutual information I SD is defined by

I SD = log 2 (1 + SN R|h SD | 2 )
The probability p out (SN R, r) can be expressed as the sum of 2 (m-1) terms p out (SN R, r) = 2 (m-1) j=1 P j where P j is given by

P j = P E j m-1 i=1
Pr[

(i) j ] (6)
where

P E j = Pr[I D ≤ R] × Pr{ m-1 i=1 [I (i) ≤ R 2 |( (i) j , I D ≤ R)]}
and the event (i) j equals the event I SRi ≤ R or I SRi > R according to the value of index j, 1 ≤ j ≤ 2 (m-1) . The probability P j in ( 6) is constituted with m components. The first component P E j is the probability denoted in (4) where each value of I (i) is conditioned to the value of I SRi . The (m -1) last terms in the product exhibit the probabilities that the I SRi are above or beyond the threshold R, for 1 ≤ i ≤ (m -1). Now, we assume that there are N passive relay terminals such that k j = [I SR k ≤ R] in P j . We define the set

K such that K = {k/ (k) j = [I SR k ≤ R], 1 ≤ k ≤ (m -1)
} with cardinality |K| = N . Thus, there are (m -1) -N active relay terminals such that (l) j = [I SR l > R] in P j . We define the set

L such that L = {l/ (l) j = [I SR l > R], 1 ≤ l ≤ (m -1)
} with cardinality |L| = (m-1)-N . Note also that 0 ≤ N ≤ (m-1). So, we have that

P E j = Pr[I D ≤ R] × Pr{ m-1 k∈K [I (k) ≤ R 2 |( (k) j , I D ≤ R)], m-1 l∈L [I (l) ≤ R 2 |( (l) j , I D ≤ R)]} (7)
For the N passive relay terminals, we have that

I (k) ≤ R 2 | (k) j ⇐⇒ I (k) ≤ R 2 |I SR k ≤ R ⇐⇒ I D ≤ R So, we have that Pr[(I (k) ≤ R 2 | (k) j )|I D ≤ R] = 1
Moreover, we have that Pr[

(k) j ] = Pr[I SR k ≤ R] = Pr[log 2 (1 + SN R|h SR k | 2 ) ≤ R]
For the (m -1) -N active relay terminals, we have that

I (l) ≤ R 2 | (l) j ⇐⇒ I (l) ≤ R 2 |I SR l > R ⇐⇒ log 2 (1 + SN R|h R l D | 2 ) ≤ R 2
and Pr[

(l) j ] = Pr[I SR l > R]
So, for high SN R values, we have a simpler expression for ( 7)

P E j = Pr[log 2 (1 + SN R|h SD | 2 ) ≤ R] × Pr{ l∈L [log 2 (1 + SN R|h R l D | 2 ) ≤ R]}
For high SN R values, the |h R l D | 2 exponential variables being mutually independent, we have that

P E j = Pr[|h SD | 2 ≤ SN R r-1 ] × l∈L Pr[|h R l D | 2 ≤ SN R r-1 ] For high SN R values, the random variables |h SR k | 2 and |h R l D | 2 being mutually independent, we have that m-1 i=1 Pr[ (i) j ] ≤ k∈K Pr[|h SR k | 2 ≤ SN R r-1 ] (8)
So, using (8), we have that

P j ≤ Pr[|h SD | 2 ≤ SN R r-1 ] × l∈L Pr[|h R l D | 2 ≤ SN R r-1 ] × k∈K Pr[|h SR k | 2 ≤ SN R r-1 ]
The

≤ SN R r-1 ]} log(SN R) = r -1 (9)
So, using (9), we have that

lim SN R→∞ log[P j ] logSN R = m(r -1)
for every j, 1 ≤ j ≤ 2 (m-1) , and

lim SN R→∞ - log[p out (SN R, r)] logSN R = m(1 -r)
Hence, the diversity gain d(r), i.e. the DMT curve of the protocol, is

d(r) = m(1 -r) (10)
So, when there are (m -1) potential relay terminals, the proposed protocol achieves the optimal DMT curve reaching the two extremes points d(0) = m and d(1) = 0 (see Figure 3). Note that the only information provided by the DMT curve is that the data rate of the overall transmission scales like a direct transmission, even in presence of a cooperative relaying.

In particular, the overheard induced by the additional signaling frames (CFC, splitting algorithm) does not appear in [START_REF] Bletsas | A simple cooperative diversity method based on network path selection[END_REF] because the DMT analysis is just providing a rough estimate of the achieved multiplexing gain.

Fig. 3. DMT curves of four protocols: the proposed protocol, the direct transmission, the protocol implementing the selection of the best relay among a set of (m -1) terminals in [START_REF] Bletsas | A simple cooperative diversity method based on network path selection[END_REF], and the on-demand relaying with one relay in [START_REF] Laneman | Cooperative diversity in wireless networks: efficient protocols and outage behavior[END_REF].

IV. CONCLUSION

The purpose of the study is the design of a DMT optimal access protocol in the context of IEEE 802.11 mesh networks. The designed protocol has two basic features: on-demand cooperation and selection of the best relay terminal. Cooperation is activated on-demand, i.e. only when a destination terminal fails in decoding the message of a source terminal. This approach allows maximization of the spatial multiplexing gain, i.e the capacity of the source-destination link. Moreover, when cooperation is needed, only the best relay terminal retransmits the source message. This allows maximization of the diversity order, i.e the robustness of the link. Hence, an optimal DMT curve is achieved. We add three other features that guarantee both a fast and an efficient relay selection. Using a splitting algorithm, the time required to select a best relay terminal is both minimized and upper bounded. Moreover, only terminals that can improve the direct transmission are pre-selected. So inefficient cooperation is now avoided. Finally, the destination terminal discards the source message when it fails to decode it. This saves processing time without sacrificing the optimality of the DMT. When (m -1) terminals are situated in the range of both a source terminal S and a destination terminal D, a diversity gain of m is provided while a spatial multiplexing gain of one is achieved. Thus, the protocol implements a DMT optimal transmission scheme. The study focusses on a selective DF transmission scheme but it can also be applied to a fixed amplify-and-forward approach.

Fig. 1 .

 1 Fig. 1. Cooperation scenario with four relay terminals.

  random variables |h SD | 2 , |h R l D | 2 for l ∈ L, and |h SR k | 2 for k ∈ K are all exponential variables. Let |h| 2 be one of these random variables. From Lemma 2 in [10], we have that

	SN R→∞ lim	log{Pr[|h| 2

We use bandwidth as a general term for resource in a communication network. Bandwidth can be expressed in time slots, frequency bands, spreading codes or space time codes.

Note that timeouts should be delayed to take into account possible cooperative transmissions.

Terminals that just receive either an ACK frame or a CFC frame ignore the signaling frame.