The Case for Liberal Spectrum Licenses: A Technical and Economic Perspective

The traditional system of radio spectrum allocation has inefficiently restricted wireless services. Alternatively, liberal licenses ceding de facto spectrum ownership rights yield incentives for operators to maximize airwave value. These authorizations have been widely used for mobile services in the U.S. and internationally, leading to the development of highly productive services and waves of innovation in technology, applications and business models. Serious challenges to the efficacy of such a spectrum regime have arisen, however. Seeing the widespread adoption of such devices as cordless phones and wi-fi radios using bands set aside for unlicensed use, some scholars and policy makers posit that spectrum sharing technologies have become cheap and easy to deploy, mitigating airwave scarcity and, therefore, the utility of exclusive rights. This paper evaluates such claims technically and economically. We demonstrate that spectrum scarcity is alive and well. Costly conflicts over airwave use not only continue, but have intensified with scientific advances that dramatically improve the functionality of wireless devices and so increase demand for spectrum access. Exclusive ownership rights help direct spectrum inputs to where they deliver the highest social gains, making exclusive property rules relatively more socially valuable. Liberal licenses efficiently accommodate rival business models (including those commonly associated with unlicensed spectrum allocations) while mitigating the constraints levied on spectrum use by regulators imposing restrictions in traditional licenses or via use rules and technology standards in unlicensed spectrum allocations.

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources

A trading model and security regime for mobile e-commerce via ad hoc wireless networking

Ad hoc wireless networking offers mobile computer users the prospect of trading with others in their vicinity anywhere anytime. This thesis explores the potential for developing such trading applications. A notable difficulty in designing their security services is being unable to use trusted parties. No one can be guaranteed present in each ad hoc wireless network session. A side benefit is that their costs don't have to be paid for. A reference model is defined for ad hoc m-commerce and a threat model is for- mulated of its security vulnerabilities. They are used to elicit security objectives and requirements for such trading systems. Possible countermeasures to address the threats are critically analysed and used to design security services to mitigate them. They include a self-organised P2P identity support scheme using PGP cer- tificates; a distributed reputation system backed by sanctions; a group membership service based on membership vouchers, quorate decisions by some group members and partial membership lists; and a security warning scheme. Security analysis of the schemes shows that they can mitigate the threats to an adequate degree to meet the trading system's security objectives and requirements if users take due care when trading within it. Formal verification of the system shows that it satisfies certain safety properties

Efficient Communication in Agent-based Autonomous Logistic Processes

Transportation of goods plays a vital role for the success of a logistics network. The ability to transport goods quickly and cost effectively is one of the major requirements of the customers. Dynamics involved in the logistics process like change or cancellation of orders or uncertain information about the orders add to the complexity of the logistic network and can even reduce the efficiency of the entire logistics process. This brings about a need of integrating technology and making the system more autonomous to handle these dynamics and to reduce the complexity. Therefore, the distributed logistics routing protocol (DLRP) was developed at the University of Bremen. In this thesis, DLRP is extended with the concept of clustering of transport goods, two novel routing decision schemes and a negotiation process between the cluster of goods and the vehicle. DLRP provides the individual logistic entities the ability to perform routing tasks autonomously e.g., discovering the best route to the destination at the given time. Even though DLRP seems to solve the routing problem in real-time, the amount of message flooding involved in the route discovery process is enormous. This motivated the author to introduce a cluster-based routing approach using software agents. The DLRP along with the clustering algorithm is termed as the cluster-based DLRP. In the latter, the goods are first clustered into groups based on criteria such as the common destination. The routing is now handled by the cluster head rather than the individual transport goods which results in a reduced communication volume in the route discovery. The latter is proven by evaluating the performance of the cluster-based DLRP approach compared to the legacy DLRP. After the routing process is completed by the cluster heads, the next step is to improve the transport performance in the logistics network by identifying the best means to transport the clustered goods. For example, to have better utilization of the transport capacity, clusters can be transported together on a stretch of overlapping route. In order to make optimal transport decisions, the vehicle calculates the correlation metric of the routes selected by the various clusters. The correlation metric aids in identifying the clusters which can be transported together and thereby can result in better utilization of the transport resources. In turn, the transportation cost that has to be paid to the vehicle can be shared between the different clusters. The transportation cost for a stretch of route is calculated by the vehicle and offered to the cluster. The latter can decide based upon the transportation cost or the selected route whether to accept the transport offer from the vehicle or not. In this regard, different strategies are developed and investigated. Thereby a performance evaluation of the capacity utilization of the vehicle and the transportation cost incurred by the cluster is presented. Finally, the thesis introduces the concept of negotiation in the cluster based routing methods. The negotiation process enhances the transport decisions by giving the clusters and the vehicles the flexibility to negotiate the transportation cost. Thus, the focus of this part of the thesis is to analyse the negotiation strategies used by the logistics entities and their role in saving negotiation time while achieving a favorable transportation cost. In this regard, a performance evaluation of the different proposed strategies is presented, which in turn gives the logistics practitioners an overview of the best strategy to be deployed in various scenarios. Clustering of goods aid in the negotiation process as on the one hand, a group of transport goods have a stronger basis for negotiation to achieve a favorable transportation price from the vehicle. On the other hand it makes it easier for the vehicle to select the packages for transport and helps the vehicle to operate close to its capacity. In addition, clustering enables the negotiation process to be less complex and voluminous. From the analytical considerations and obtained results in the three parts of this thesis, it can be concluded that efficient transport decisions, though very complex in a logistics network, can be simplified to a certain extent utilizing the available information of the goods and vehicles in the network

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

Cryptography and Computer Communications Security. Extending the Human Security Perimeter through a Web of Trust

This work modifies Shamir’s algorithm by sharing a random key that is used to lock up the secret data; as against sharing the data itself. This is significant in cloud computing, especially with homomorphic encryption. Using web design, the resultant scheme practically globalises secret sharing with authentications and inherent secondary applications. The work aims at improving cybersecurity via a joint exploitation of human factors and technology; a human-centred cybersecurity design as opposed to technology-centred. The completed functional scheme is tagged CDRSAS. The literature on secret sharing schemes is reviewed together with the concepts of human factors, trust, cyberspace/cryptology and an analysis on a 3-factor security assessment process. This is followed by the relevance of passwords within the context of human factors. The main research design/implementation and system performance are analysed, together with a proposal for a new antidote against 419 fraudsters. Two twin equations were invented in the investigation process; a pair each for secret sharing and a risk-centred security assessment technique. The building blocks/software used for the CDRSAS include Shamir’s algorithm, MD5, HTML5, PHP, Java, Servlets, JSP, Javascript, MySQL, JQuery, CSS, MATLAB, MS Excel, MS Visio, and Photoshop. The codes are developed in Eclipse IDE, and the Java-based system runs on Tomcat and Apache, using XAMPP Server. Its code units have passed JUnit tests. The system compares favourably with SSSS. Defeating socio-cryptanalysis in cyberspace requires strategies that are centred on human trust, trust-related human attributes, and technology. The PhD research is completed but there is scope for future work.Petroleum Technology Development Fund (PTDF), Abuja, Nigeria