IT or digitally driven supply chain networks (DSCNs) are experiencing a steady increase of cybersecurity threats. The sharing of cyber threat intelligence (CTI) is vital for proactive risk management and trustworthiness against these threats by DSCN enterprises. However, a major obstacle to implement this sharing is enterprise aversion to share quality CTI due to competitive concerns. As a first, we propose a strategic game-theoretic rubric with spillover effects for providing operational insights on effective CTI information sharing between such enterprises in a DSCN. We show that enterprise Katz-Bonacich (KB) network centrality together with the strategic spillover effects of shared CTI dictate (i) a DSCN enterprise’s Nash optimal CTI sharing quality at a unique Nash equilibrium (NE) and (ii) the price of anarchy (PoA) at the NE denoting the additional sharing welfare improvement collectively possible for the DSCN enterprises. We apply spectral graph theory, to practically operationalize this improvement, that suggests incentivizing enterprises to improve CTI sharing quality proportional to their KB centralities at the NE. Our research ideas contribute to trustworthy DSCNs.

SIGSIM-PADS ’25, Santa Fe, NM, USA

Permissionless blockchain consensus protocols have been leveraged for defining decentralized economies for the (commercial or private) trade of virtual and physical assets, using cryptocurrencies. In most instances, the assets being traded are regulated , which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formalize proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in illegal activities in the associated crypto-economies. In this contribution, unlike existing non-consensus solutions, which are prone to be more compute-time and audit-time intensive, we present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain consensus. As per our regulatory framework, we derive, through a stochastic game analysis, block proposal strategies under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that when a majority of the consensus protocol participants are licensed by the regulator to propose legal transactions, there exists a fair consensus execution policy to maximize the legal transaction throughput in the blockchain network.

With the advent of 5G+ services, it has become increasingly convenient for mobile users to enjoy high-quality multimedia content from CDN driven streaming and catch-up TV services (Netflix, iPlayer) in the (post-) COVID over-the-top (OTT) content rush. To relieve ISP owned fixed-line networks from CDN streamed multimedia traffic, system ideas (e.g., Wi-Stitch in [ 45 ]) have been proposed to (a) leverage 5G services and enable consumers to share cached multimedia content at the edge, and (b) consequently, and more importantly, reduce IP traffic at the core network. Unfortunately, given that contemporary multimedia content might be a monetized asset, these ideas do not take this important fact into account for shared content. We present EdgeMart —a content provider (CP) federated, and computationally sustainable networked (graphical) market economy for paid-sharing of cached licensed (OTT) content with autonomous users of a wireless edge network (WEN). EdgeMart is a unique oligopoly multimedia market (economy) that comprises competing networked sub-markets of non-cooperative content sellers/buyers—each sub-market consisting of a single buyer connected (networked) to only a subset of sellers. We prove that for any WEN-supported supply-demand topology , a pure strategy EdgeMart equilibrium exists that is (a) nearly efficient (in a microeconomic sense) indicating economy sustainability, (b) robust to edge user entry/exit, and (c) can be reached in poly-time (indicating computational sustainability). In addition, we experimentally show that for physical WENs of varying densities, a rationally selfish EdgeMart economy induces similar orders of multimedia IP traffic savings when compared to the ideal (relatively less practical), altruistic , and non-monetized “economy” implemented atop the recently introduced Wi-Stitch WEN-based content trading architecture. Moreover, the EdgeMart concept helps envision a regulated edge economy of opportunistic (pay per licensed file) client services for commercial OTT platforms.

Third-party residual cyber-risk management (RCRM) services (e.g., insurance, re-insurance) are getting increasingly popular (currently, a multi-billion-dollar annual market) with C-suites managing industrial control systems (ICSs) based upon IoT-driven cyber-physical IT and OT technology. Apart from mitigating and diversifying losses from (major) cyber-threats RCRM services positively contribute to improved cyber-security as an added societal benefit. However, it is also well known that RCRM markets (RCRM for ICSs being a mere subset) are relatively nascent and sparse. There is a huge (approximately 10-fold) supply-demand gap in an environment where (a) annual cyber-losses range in trillions of USD, and (b) CRM markets (residual or otherwise) are annually worth only up to 0.25 trillion USD. The main reason for this wide gap is the age-old information asymmetry (IA) bottleneck between the demand and supply sides of the third-party RCRM market, which is significantly amplified in modern cyber-space settings. This setting primarily comprises interdependent and intra-networked ICSs (and/or traditional IT systems) from diverse application sectors inter-networked with each other in a service supply-chain environment.In this article, we are the first to prove that optimal cyber-risk diversification (integral to RCRM) under IA is computationally intractable, i.e., NP-hard, for such (systemic) inter-networked societies.Here, the term “optimal diversification” implies the best way a residual and profit-minded cyber-risk manager can form a portfolio of client coverage contracts. We follow this up with the design and analysis of a computational policy that alleviates this intractability challenge for the social good. Here, the social good can be ensured through denser RCRM markets that in principle improve cyber-security. Our work formally establishes (a) the reason why it has been very difficult in practice (without suitable policy intervention) to densify IA-affected RCRM markets despite their high demand in modern CPS/ICS/IoT societies; and (b) the efficacy of our computational policy to mitigate IA issues between the supply and demand sides of an RCRM market in such societies.

IoT-driven smart societies are modern service-networked ecosystems, whose proper functioning is hugely based on the success of supply chain relationships. Robust security is still a big challenge in such ecosystems, catalyzed primarily by naive cyber-security practices (e.g., setting default IoT device passwords) on behalf of the ecosystem managers, i.e., users and organizations. This has recently led to some catastrophic malware-driven DDoS and ransomware attacks (e.g., the Mirai and WannaCry attacks). Consequently, markets for commercial third-party cyber-risk management (CRM) services (e.g., cyber-insurance) are steadily but sluggishly gaining traction with the rapid increase of IoT deployment in society, and provides a channel for ecosystem managers to transfer residual cyber-risk post attack events. Current empirical studies have shown that such residual cyber-risks affecting smart societies are often heavy-tailed in nature and exhibit tail dependencies. This is both, a major concern for a profit-minded CRM firm that might normally need to cover multiple such dependent cyber-risks from different sectors (e.g., manufacturing and energy) in a service-networked ecosystem, and a good intuition behind the sluggish market growth of CRM products. In this article, we provide: 1) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies and 2) a real-data-driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails. To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate CRM.

As authors of our recently accepted article:Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes, published in the IEEE IoT Journal, we regret that we have found a few errors in the numerical evaluation setup of the works in[1]and[2]that we had borrowed for our accepted paper. In this correction statement, we describe the errors in detail, correct it, and present our revised results with a renewed experimental setup, hoping it to replace the existing incorrect numerical results in the accepted paper. We apologize for the inconvenience caused to the reader. We emphasize that the numerical evaluation section does not in any way hamper the theoretical contributions in this article, and was initially only meant to provide some empirical evidence for whether the theory proposed in this article generalizes to behavioral settings introduced in[2].

In-app advertising is a multi-billion dollar industry that is an essential part of the current digital ecosystem, and is amenable to sensitive consumer information often being sold downstream without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy (IP) breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this article is: does the type of information being traded downstream play a role in the degree of IP risks generated? We investigate two general (one-many) information trading market structures between a single data aggregating seller (e.g., enterprise app) and multiple competing buyers (e.g., ad-networks, retailers), distinguished by mutually exclusive and privacy sanitized aggregated consumer data (information) types: (i) data entailing strategically complementary actions among buyers and (ii) data entailing strategically substituting actions among buyers. Our primary question of interest here is: trading which type of data might pose less information privacy risks for society? To this end, we show that at market equilibrium IP trading markets exhibiting strategic substitutes between buying firms pose lesser risks for IP in society, primarily because the `substitutes' setting, in contrast to the `complements' setting, economically incentivizes appropriate consumer data distortion by the seller in addition to restricting the proportion of buyers to which it sells. Moreover, we also show that irrespective of the data type traded by the seller, the likelihood of improved IP in society is higher if there is purposeful or free-riding based transfer/leakage of data between buying firms. This is because the seller finds itself economically incentivized to restrict the release of sanitized consumer data with respect to the span of its buyer space, as well as in improved data quality.

Small ‘boutique’ clouds challenging the big three (AWS, Azure, Google Cloud) on speed, cost, flexibility, on-prem, and hybrid cloud options are slowly on the rise. This paper comments on the work by Palet al., in 2020, in relation to the efficiency of practical federated small cloud (SC) resource (e.g., VMs) sharing market structures at a market equilibrium. While the work by Palet al., in 2020, guarantees a unique, stable, and efficient sharing equilibrium, it falls short of providing a microscopic view into practically likely sharing network structures (graphs) among SC providers in the market and their effect on market efficiency. Consequently, we envision a graphical federated cloud sharing market and comment on its efficiency guarantee at the market equilibrium. While a symmetric graphical small cloud resource sharing economy is pure-strategy efficient (like in the work by Palet al., in 2020) an asymmetric one generates inefficiencies that improves with an increased number of SCs in the sharing market.

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature?. In this article, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.

Despite the promising potential of network risk management services (e.g., cyber-insurance) to improve information security, their deployment is relatively scarce, primarily due to such service companies being unable to guarantee profitability. As a novel approach to making cyber-insurance services more viable, we explore a symbiotic relationship between security vendors (e.g., Symantec) capable of price differentiating their clients, and cyber-insurance agencies having possession of information related to the security investments of their clients. The goal of this relationship is to (i) allow security vendors to price differentiate their clients based on security investment information from insurance agencies, (ii) allow the vendors to make more profit than in homogeneous pricing settings, and (iii) subsequently transfer some of the extra profit to cyber-insurance agencies to make insurance services more viable. In this paper, we perform a theoretical study of a market for differentiated security product pricing, primarily with a view to ensuring that security vendors (SVs) make more profit in the differentiated pricing case as compared to the case of non-differentiated pricing. In order to practically realize such pricing markets, we propose novel andcomputationally efficientconsumer differentiated pricing mechanisms for SVs based on (i) the market structure, (ii) the communication network structure of SV consumers captured via a consumer'sBonacich centralityin the network, and (iii) security investment amounts made by SV consumers. We validate our analytical model via extensive simulations conducted on practical SV client network topologies; main results show (through those simulations) that (a) amonopolySV could improve its profit margin by upto$\approx$25 percent (based on the simulation setting) by accounting for clients’ investment information and network locations, whereas in anoligopolysetting, SVs could improve their profit margins by upto$\approx$18 percent, and (b) differentiated security pricing mechanisms are fair among SV consumers with respect to the total investment made by a consumer. To the best of knowledge, the proposed differentiated pricing framework is the first of its kind in the security products domain, and is generally applicable to usecases beyond the one investigated in this work.

In developing countries, reliable electricity access is often undermined by the absence of supply from the national power grid and/or load shedding. To alleviate this problem, smart micro-grid (SMG) networks that are small scale distributed electricity provision networks composed of individual electricity providers and consumers, are being increasingly deployed. To ensure the reliable operation of SMGs, monitoring is necessary for data collection and state estimation processes. However, highly calibrated and trustworthy smart meters that are ideally suited to perform such monitoring tasks are often costly and non-ideally suited to SMGs which operate under unreliable communication network infrastructures. As a result, SMGs are an easy target to an adversary who can very easily gain access to private information by monitoring transmission between nodes in the SMG network, and launch inference-based privacy attacks. These attacks lead to electricity theft and grid instability problems in the SMG. The widely popular differential privacy (DP) technique (a rigorous technique in the family of privacy-preserving data publishing (PPDP) techniques to mathematically guarantee the preservation of data privacy) does not address multi-attribute correlations, that are inherently exploited by an adversary in inference attacks. In this paper, we propose HIDE, an oblivious computationally efficient, and rigorous information-theoretic privacy engineering framework for datasets/databases arising in the SMG environments that robustly accounts for multi-attribute correlations while preserving data privacy in a provably optimal fashion. A salient and powerful advantage of HIDE is its ability to generate optimal utility-privacy tradeoffs (computationally efficiently) when the privacy preserving entity in the worst case might have no prior statistical information that links a user's private data with his public data.

Small-scale clouds (SCs) often suffer from resource under-provisioning during peak demand, leading to inability to satisfy service level agreements (SLAs) and consequent loss of customers. One approach to address this problem is for a set of autonomous SCs to share resources among themselves in a cost-induced cooperative fashion, thereby increasing their individual capacities (when needed) without having to significantly invest in more resources. In this context, a central problem is how to properly share resources for a price in order to achieve profitable service, while maintaining customer SLAs. To address this problem, we propose the SC-Share framework that utilizes two interacting models: (i) a stochastic performance model that estimates the achieved performance characteristics under given SLA requirements, and (ii) a market-based game-theoretic model that (as shown empirically) converges to efficient resource sharing decisions at market equilibrium. Our results include extensive evaluations that illustrate the utility of the proposed framework.

Cyber-physical systems (CPSs) integrate computation, communication, and physical capabilities to interact with the physical world and humans. In this paper, we develop a novel strategic resource availability management (STREAM) system to improve information integrity and availability in an energy constrained CPS environment under the presence of malicious adversaries. The term “resource” here can be any component of a CPS. The main elements of STREAM are: 1) difficult but realistic “repeated (adversary-defender) game” settings and 2) a set of provably optimal defender strategies plus effective heuristics, against equally potent adversary moves. STREAM is based on the concept of dynamic games in sequential game theory, and is the first system to incorporate the realistic behavioral aspect that in many CPSs, both, the class of adversaries, as well as the class of CPS protectors, could move in a covert and stealthy manner in order to outwit the other in the war on “resource control.” In order to demonstrate the effectiveness of STREAM strategies to improve CPS resource availability to the nonadversary, we first conduct a thorough theoretical analysis on a model smart grid CPS as a representative example of a CPS. We then follow up the analysis with an extensive simulation study on the standard IEEE 14 smart power grid architecture. The results show that STREAM strategies improve smart grid system integrity and availability by approximately upto 67% when compared to nonstrategic approaches. Our proposed (analysis, simulation) suite for the grid is extendible to general CPS application domains.

Consistent (pause-free) quality of service is required in peer-to-peer (P2P) video streaming systems. In this paper, we aim to eliminate the problem of playback pauses in such systems via the use of positive incentives for peers to contribute high upload rates. We model our problem as a market, where the market stakeholders consist of multiple content providers, advertisement providers, and network peers; the positive incentives for peers in the market are reduced advertisement (ad) viewing durations. From a system design perspective, one of our primary goals is to compute the market equilibria that include appropriate ad viewing durations, offering sufficient incentives for network peers to continue contributing. Our simulation-based studies demonstrate that we mitigate the “playback pause” problem for peers by up to 80% as compared to existing approaches, generate sufficient utility for advertisers to be part of the market, and enable content providers to achieve their desired utility by providing sufficient incentives for all peers to stay in the system without violating ad provider agreements.

Prosumers or proactive consumers are steadily on the rise in emerging Smart Grid systems. These consumers, apart from their traditonal role of using energy from the grid, also are actively involved in individually transferring stored energy from renewable sources such as wind and solar, to the grid. The large-scale integration of renewable generation in the emerging grid will re-define ways of meeting consumer energy demands, and more importantly drive greener and cost-effective utility operations. In this paper, we investigate the problem of matching consumer demand with the grid supply in real-time, and in the presence of renewables. We formulate this problem as a stochastic optimization problem and propose MATCH, a fast distributed real-time algorithm that accounts for the uncertainties in (i) renewable generation, (ii) the latter's transmission through the grid network, (iii) loads, and (iv) energy prices, and balances power in the Smart Grid at all times. MATCH is based on the Lyapunov stochastic optimization framework and scales to localities with a large number of networked renewable generation sources. We validate the efficacy of MATCH through experiments conducted using data modelled on proprietary data obtained from two public utilities. As part of the main results of this work, we show that (a) MATCH outputs unique approximate-optimal grid parameter configuration vectors in real-time that ensure perennial supplydemand balance in the grid at a minimum cost, and (b) mesh transmission network topologies lead to better MATCH outputs when compared to other existing transmission network topologies.

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in aprovably optimalreal-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online)randomized approximation algorithmfor grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.

Inconsistent quality of service is a significant problem in P2P-based video streaming systems. Pauses in playback are common for low capacity peers as they often upload relatively little compared to high capacity peers, and thus suffer from the `lack of reciprocity' problem. In this work, we propose an Ad-driven Streaming P2p ECosysTem (ASPECT) that aims to eliminate the problem of playback pauses by adopting `reduced advertisement viewing duration' as a positive incentive for peers to provide high upload rates. ASPECT rewards high capacity peers by reducing their advertisement viewing duration, when they provide more opportunities for lower capacity peers to download data. We build our research problem on a utility-theoretic market-based model, where the market stakeholders consist of a content provider, an advertisement provider, and network peers. Using concepts from game theory, we determine the system parameters to reach market efficiency, and study the practical implications of equilibria on the satisfaction of stakeholders' interests. From a system design perspective, one of our primary goals is to compute the equilibria advertisement viewing durations, that offer sufficient incentives for network peers to continue contributing. We evaluate ASPECT through an extensive simulation-based study. The results demonstrate that ASPECT mitigates the `playback pause' problem for peers by at least 80% compared to existing approaches, results in appropriate advertisement viewing durations for all peers based on their contributions, and at the same time generates sufficient profit for the advertiser to be part of the market.

Recent work in security has illustrated that solutions aimed at detection and elimination of security threats alone are unlikely to result in a robust cyberspace. As an orthogonal approach to mitigating security problems, some have pursued the use of cyber-insurance as a suitable risk management technique. Such an approach has the potential to jointly align with the incentives of security vendors (e.g., Symantec, Microsoft, etc.), cyber-insurers (e.g., ISPs, cloud providers, security vendors, etc.), regulatory agencies (e.g., government), and network users (individuals and organizations), in turn paving the way for comprehensive and robust cyber-security mechanisms. To this end, in this work, we are motivated by the following important question: can cyber-insurance really improve the security in a network? To address this question, we adopt a market-based approach. Specifically, we analyze regulated monopolistic and competitive cyber-insurance markets, where the market elements consist of risk-averse cyber-insurers, risk-averse network users, a regulatory agency, and security vendors. Our results show that (i) without contract discrimination amongst users, there always exists a unique market equilibrium for both market types, but the equilibrium is inefficient and does not improve network security, and (ii) in monopoly markets, contract discrimination amongst users results in a unique market equilibrium that is efficient, which in turn results in network security improvement - however, the cyber-insurer can make zero expected profits. The latter fact is often sufficient to de-incentivize the insurer to be a part of a market, and will eventually lead to its collapse. This fact also emphasizes the need for designing mechanisms that incentivize the insurer to permanently be part of the market.

Internet users such as individuals and organizations are subject to different types of epidemic risks such as worms, viruses, and botnets. To reduce the probability of risk, an Internet user generally invests in self-defense mechanisms like antivirus and antispam software. However, such software does not completely eliminate risk. Recent works have considered the problem of residual risk elimination by proposing the idea of cyber-insurance. In this regard, an important decision for Internet users is their amount of investment in self-defense mechanisms when insurance solutions are offered. In this paper, we investigate the problem of self-defense investments in the Internet, under full and partial cyber-insurance coverage models. By the term `self-defense investment', we mean the monetary-cum-precautionary cost that each user needs to invest in employing risk mitigating self-defense mechanisms, given that it is fully or partially insured by the Internet insurance agencies. We propose a general mathematical framework by which co-operative and non-co-operative Internet users can decide whether or not to invest in self-defense for ensuring both, individual and social welfare. Our results show that (1) co-operation amongst users results in more efficient self-defense investments than those in a non-cooperative setting, under a full insurance coverage model and (2) partial insurance coverage motivates non-cooperative Internet users to invest more efficiently in self-defense mechanisms when compared to full insurance coverage.

Modern day wireless networks are increasingly supporting various civilian applications that require high bandwidth for successful operation. In such cases, proper bandwidth utilization is essential for good network performance. In this paper, we address the problem of routing a given traffic demand matrix in a multi-hop wireless mesh network such that the total network load is fairly distributed amongst various wireless links. Using lexicographic optimization, we develop a polynomial time multi-path load balanced routing scheme that achieves leximax minimization and distributes the total network load fairly in a min-max sense. We use linear programming to formulate our problem and perform extensive simulations to highlight the performance of our routing scheme in achieving optimal load balance whilst satisfying user requirements.

Modern day wireless networks are increasingly supporting various civilian applications that require high bandwidth for successful operation. In such cases, it is important to ensure proper maintenance of links that contribute the most to achieving the required level of service. In this paper, we devise a method to analytically characterize the importance of each link in a multichannel, multi-radio, multi-rate wireless mesh network in satisfying quality-of-service (QoS) requirements of active flows. We propose link importance metrics that can guide network/traffic engineers in properly provisioning the network to handle failures of critical links that contribute significantly to the achievability of flow demands. We consider single-commodity flows and perform simulations to evaluate the quality of our metrics.

This paper addresses the problem of evaluating the reliability of a single-radio, multi-channel, multi-hop dynamic spectrum access (DSA) network supporting QoS-driven network services. We define reliability as the reciprocal of the probable number of transmission attempts required to ship each unit of application data successfully under a given demand. The demand represents the flow rate (per unit of time) at which an application needs to be served. We provide analytical methods of reliability evaluation for two classes of DSA network service. 1. A network servicing a single source-destination pair and 2. A network servicing a broadcasting or multicasting application where common data has to be disseminated to all or a subset of nodes in the network. All the nodes involved in data communication are secondary nodes, which access the channels reserved for primary users, in an unlicensed manner. Our methods take into account the channel capacities of the links and provide an useful and an important metric for the effectiveness of a dynamic spectrum access network for a given demand. These methods can also be used to find robust routing paths in such networks. The approach we use is based on concepts in mathematical theory of reliability and theory of network flows. Our methodologies are scalable, simple to implement and can be extended to all other types of communication networks and network applications. To the best of our knowledge, this is the first work to address reliability issues in DSA networks.

It has been shown that use of directional antenna in the context of ad hoc wireless networks can largely reduce radio interference, thereby improving the utilization of wireless medium. However, that alone does not always guarantee improvement in overall system performance. In this paper, we have identified several criteria and investigated their interrelationships and impact on overall system performance in this context. Our methodology uses optimisation techniques using multicriteria decision analysis. We use analytic hierarchy process (AHP) to identify relative weights of different criteria under different application-specific scenario in order to solve the optimisation problem for each scenario by TOPSIS approach. The result shows that the parameter setting required to get optimum performance is application-specific; depending on the situation or application-scenario, several parameters need to be controlled to get better system performance.