Wireless Sensor Network (WSN) localization is an important and fundamental problem that has received a lot of attention from the WSN research community. Determining the absolute and relative coordinate of sensor nodes in the network adds much more meaning to sense data. The research community is very rich in proposals to address this challenge in WSN. This paper, explores the various techniques proposed to address the acquisition of location information in WSN. The paper also evaluate the performance of these techniques based on the energy consumption, the skill and man hours needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. KeywordsWireless sensor network, sensor nodes, localization centralized and distributed algorithm, range-based and rangefree algorithm.

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed.

The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents.

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The number of anchor nodes required for accurate localization is an important problem in the wireless sensor network research community. The error associated with localization is high when anchor nodes are not optimally placed in the network. No matter how the network is set up, the error associated with localization is inevitable. There are various sources for these errors, one of which is the unavailability of anchor nodes. These conditions arise due to stumpy deployment density or poor signal propagation owing to factors like multipath effects, fading effects and poor visibility. This paper proposes a method of determining the minimum number of anchor nodes required for a given sensor (smart energy meter) network dimension using triangulation as the localization process. The proposed method uses the sensitivity of the sensor nodes and various environmental conditions. Using the sensitivity of the sensor nodes and the environmental conditions, the minimum number of anchor nodes for a network dimension was determined through simulation. The minimum number of anchors required for a network with clear line of sight, suburban , residential and non-line of sight environments was achieved with this method.

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Social Internet of Things (SIoT) involves integrating social networking concepts in the Internet of Things (IoT) to enhance social interactions among IoT objects and users. SIoT is envisaged to provide adequate service selection and discovery. Trust is an essential factor whenever social concepts are discussed in communication networks. Trust usually leads to a mutual relationship between two parties (i.e., the trustor and trustee) where they both enjoy mutual benefits. For secure social relationships, Trust management (TM) is a crucial feature of SIoT. The primary aim of this work is to provide a comprehensive review of trust management proposals/schemes available for SIoT. Four main trust calculation algorithms for trust management were selected for this review, and they were examined in detail. The IEEE Xplore, Scopus, ResearchGate, and Google Scholar databases were searched for articles containing the terms "Trust aggregation approaches in IoT", and "Trust computation in SIoT" with a particular emphasis on works published between 2018 and 2021. The paper also discussed the pros and cons of each TM technique, trust metrics/features, contributions, and limitations of the state-of-the-art SIoT TM proposals in the literature. The paper further provides open issues and possible research directions for entry-level researchers in the domain of SIoT.

With the exponential increase in connected devices and its accompanying complexities in network management, dynamic Traffic Engineering (TE) solutions in Software-Defined Networking (SDN) using Reinforcement Learning (RL) techniques has emerged in recent times. The SDN architecture empowers network operators to monitor network traffic with agility, flexibility, robustness and centralized control. The separation of the control and the forwarding plane in SDN has enabled the integration of RL agents in the networking architecture to enforce changes in traffic patterns during network congestions. This paper surveys major RL techniques adopted for efficient TE in SDN. We reviewed the use of RL agents in modelling TE policies for SDNs, with agents' actions on the environment guided by future rewards and a new state. We further looked at the SARL and MARL algorithms the RL agents deploy in forming policies for the environment. The paper finally looked at agents design architecture in SDN and possible research gaps.