Software defined wireless sensor load balancing routing for internet of things applications: Review of approaches.

The proliferation of the Internet of Things (IoT) devices has led to a surge in Internet traffic characterized by variabilities in Quality of Service (QoS) demands. Managing these devices and traffic effectively proves challenging, particularly within conventional IoT network architectures lacking centralized management. However, the advent of Software-Defined Networking (SDN) presents intriguing opportunities for network management, capable of addressing challenges in traditional IoT architectures. SDN's ability to provide centralized network management through a programmable controller, separate from data forwarding elements, has led researchers to incorporate SDN features with IoT (SDIoT) and Wireless Sensor Networks (SDWSN) ecosystems. However, despite the SDN support, these networks encounter challenges related to load-imbalance routing issues, as the SDN controller may be constrained while certain access points serving end users become overloaded. In response to these challenges, various load-balancing routing solutions have been proposed, each with distinct objectives. However, a comprehensive study that classifies and analyzes these solutions based on their weaknesses and postmortem challenges is currently lacking. This paper fills this gap by providing an in-depth classification of existing solutions. The study categorizes the problems addressed by different schemes and summarizes their findings. Furthermore, it discusses the shortcomings of current studies, and postmortem challenges associated with integrating SDN with IoT, and suggests future research directions.

Formal Analysis of Trust and Reputation for Service Composition in IoT.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

Review of Learning-Based Robotic Manipulation in Cluttered Environments.

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.