Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption.

One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the Tactile Internet will become a real. One way to overcome the 1 ms latency is to employ a centralized controller in the core of the network with a global knowledge of the system, together with the concept of network function virtualization (NFV). This is the idea behind the software defined networking (SDN). This paper introduces a Tactile Internet system structure, which employs SDN in the core of the cellular network and mobile edge computing (MEC) in multi-levels. The work is mainly concerned with the structure of the core network. The system is simulated over a reliable environment and introduces a round trip latency of orders of 1 ms. This can be interpreted by the reduction of intermediate nodes that are involved in the communication process.

After more than two decades of their introduction, Wireless Sensor Networks (WSNs) remain an active research topic due to their wide range applications in areas such as healthcare, military, monitoring and surveillance systems. In most applications, sensor nodes are constrained in energy supply and communication bandwidth. Therefore, novel techniques to reduce energy inefficiencies and for efficient use of the limited bandwidth resources are essential. Such constraints combined with dense network deployment pose several challenges to the design and management of WSNs and require energy-awareness at all layers of the networking protocol stack. For instance, at the Data-Link layer, low duty cycle Medium Access Control (MAC) protocols trade off latency for energy efficient operation. In this paper, we present a survey of state-of-the-art low duty cycle MAC protocols. We first outline the design challenges for MAC protocols in WSNs. Then, we present a comprehensive survey of the most prominent and recent MAC protocols. These protocols are classified into synchronous and asynchronous based on their mode of operation. Finally, the paper highlight open research problems in MAC layer for WSNs

Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from $0.979\%$ and gradually decreases to $0.87\%$ . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.