Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energyhole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

Machine-to-Machine (M2M) communication is the leading technology for realising the Internet-of-Things (IoT). The M2M sensor nodes are characterized by low-power and low-data rates devices which have increased exponentially over the years. IPv6 over low power wireless personal area network (6LoWPAN) is the first protocol that provides IPv6 connectivity to the wireless M2M sensor nodes. Having a tremendous number of M2M sensor nodes execute independent control decision leads to difficulty in network control and management. In addition, these evergrowing devices generate massive traffic and cause energy scarcity, which affects the M2M sensor node lifetime. Recently, software defined-networking (SDN) and network functioning virtualization (NFV) are being used in M2M sensor networks to add programmability and flexibility features in order to adopt the exponential increment in wireless M2M traffic and enable network configuration even after deployment. This paper presents a proof-of-concept implementation which aims to analyze how SDN, NFV, and cloud computing can interact together in the 6LoWPAN gateway to provide simplicity and flexibility in network management. The proposed approach is called customized software defined-NFV (SD-NFV), and has been tested and verified by implementing a real-time 6LoWPAN testbed. The experimental results indicated that the SD-NFV approach reduced the network discovery time by 60% and extended the node's lifetime by 65% in comparison to the traditional 6LoWPAN network. The implemented testbed has one sink which is the M2M 6LoWPAN gateway where the network coordinator and the SDN controller are executed. There are many possible ways to implement 6LoWPAN testbed but limited are based on open standards development boards (e.g., Arduino, Raspberry Pi, and Beagle Bones). In the current testbed, the Arduino board is chosen and the SDN controller is customized and written using C++ language to fit the 6LoWPAN network requirements. Finally, SDN and NFV have been envisioned as the most promising techniques to improve network programmability, simplicity, and management in cloud-based 6LoWPAN gateway.

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This paper introduces a proof-of-concept 6LoWPAN testbed to study the integration of programmable network technologies in relaxed throughput and low-power IoT devices. Open source software and hardware platforms are used in the implemented testbed to increase the possibility of future network extension. The proposed architecture offers end-to-end connectivity via the 6LoWPAN gateway to integrate IPv6 hosts and the low data rate devices directly. Nowadays, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are the most promising technologies for dealing with the massive increase in M2M devices and achieving agile traffic. The developed approach in this paper is entitled tailored Software Defined-Network Function Virtualization (SD-NFV), which is aimed at reducing the end-to-end delay and improving the energy depletion in sensor nodes. Experimental scenarios of the implemented testbed are conducted using a simple sensing application and the obtained results indicate that the introduced approach is appropriate for constrained IoT devices. By utilizing SD-NFV scheme in 6LoWPAN network, the data delivery ratio increased by 5-14%, the node operational time prolonged by 70%, the end-to-end latency for gathering sensor data minimized by ≈160%, and the latency for transmitting control messages to a specified node diminished by ≈63% when compared to a traditional (non SDN-enabled) 6LoWPAN network.

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