QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%

Energy efficient cooperative coalition selection in cluster-based capillary networks for CMIMO IoT systems

The Cooperative Multiple-input-multiple-output (CMIMO) scheme has been suggested to extend the lifetime of cluster heads (CHs) in cluster-based capillary networks in Internet of Things (IoT) systems. However, the CMIMO scheme introduces extra energy overhead to cooperative devices and further reduces the lifetime of these devices. In this paper, we first articulate the problem of cooperative coalition’s selection for CMIMO scheme to extend the average battery capacity among the whole network, and then propose to apply the quantum-inspired particle swarm optimization (QPSO) to select the optimum cooperative coalitions of each hop in the routing path. Simulation results proved that the proposed QPSO-based cooperative coalition’s selection scheme could select the optimum cooperative sender and receiver devices in every hop dynamically and outperform the virtual MIMO scheme with a fixed number of cooperative devices

PERFORMANCE STUDY FOR CAPILLARY MACHINE-TO-MACHINE NETWORKS

Communication technologies witness a wide and rapid pervasiveness of wireless machine-to-machine (M2M) communications. It is emerging to apply for data transfer among devices without human intervention. Capillary M2M networks represent a candidate for providing reliable M2M connectivity. In this thesis, we propose a wireless network architecture that aims at supporting a wide range of M2M applications (either real-time or non-real-time) with an acceptable QoS level. The architecture uses capillary gateways to reduce the number of devices communicating directly with a cellular network such as LTE. Moreover, the proposed architecture reduces the traffic load on the cellular network by providing capillary gateways with dual wireless interfaces. One interface is connected to the cellular network, whereas the other is proposed to communicate to the intended destination via a WiFi-based mesh backbone for cost-effectiveness. We study the performance of our proposed architecture with the aid of the ns-2 simulator. An M2M capillary network is simulated in different scenarios by varying multiple factors that affect the system performance. The simulation results measure average packet delay and packet loss to evaluate the quality-of-service (QoS) of the proposed architecture. Our results reveal that the proposed architecture can satisfy the required level of QoS with low traffic load on the cellular network. It also outperforms a cellular-based capillary M2M network and WiFi-based capillary M2M network. This implies a low cost of operation for the service provider while meeting a high-bandwidth service level agreement. In addition, we investigate how the proposed architecture behaves with different factors like the number of capillary gateways, different application traffic rates, the number of backbone routers with different routing protocols, the number of destination servers, and the data rates provided by the LTE and Wi-Fi technologies. Furthermore, the simulation results show that the proposed architecture continues to be reliable in terms of packet delay and packet loss even under a large number of nodes and high application traffic rates

Joint Machine-Type Device Selection and Power Allocation for Buffer-Aided Cognitive M2M Communication

In this paper, a cognitive machine-to-machine (M2M) communication network is considered, in which a cellular network shares the spectrum with the M2M communication network with M machine-type devices (MTDs), one half-duplex relay, and one MTD gateway for data gathering. One key challenge is that in the future 5G wireless networks, there will be billions of those small MTDs, and therefore, a MTD selection protocol is required for managing data transmission between MTDs. A joint buffer-aided MTD selection and power allocation protocol is proposed to maximize the MTDs’ sum-rate provided that the induced interference to the cellular network is limited. In particular, in the proposed scheme, at each time slot and each subcarrier, the cognitive M2M network optimally decides on whether to be silent or to select either the relay or one of the MTDs for data transmission. To this end, for each MTD, there exists a buffer at the relay to avoid data loss. The closed-form expressions for the power coefficients of MTDs are calculated. Simulation results show that the proposed policy improves the sum-rate of the CM2M network in comparison with the other proposed schemes for M2M communication without buffe

Delay Analysis of Network Architectures for Machine-to-Machine Communications in LTE System

Machine-to-machine communications has emerged to provide autonomic communications for a wide variety of intelligentservices and applications. Among different communication technologies available for connecting machines, cellular-basedsystems have gained more attention as backhaul networks due to ubiquitous coverage and mobility support. The diverse ranges of service requirements as well as machine constraints require adopting different network architectures. This paper reviews three M2M network architectures to integrate machines into the LTE system and analyzes their associated communication delays. It also presents how the appropriate networks can be selected for some machine-to-machine applications, fulfilling their latency constraints.Peer reviewe

QoS-aware Energy Efficient Cooperative Scheme for Cluster-based IoT Systems

The Internet of Things (IoT) technology with huge number power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device, however, this could also degrade the Quality of Service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multi-objective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then we propose to combine the Quantum Particle Swarm Optimization (QPSO) with the improved Non-dominated Sorting Genetic algorithm (NSGA-II) to obtain the Pareto optimal front. In particular, NSGA-II is applied to solve the formulated multi-objective optimization problem and QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance

On the energy savings achieved through an internet of things enabled smart city trial

Improving efficiency of city services and facilitating a more sustainable development of cities are the main drivers of the smart city concept. This paper describes a field trial that instantiates a novel architecture exploiting major concepts from the Future Internet (FI) paradigm. The trial has been executed in one of the parks of the city of Santander providing an autonomous public street lighting adaptation service. The trial takes advantage of both the critical communications infrastructures already in place and owned by the utility as well as of the Internet of Things infrastructure belonging to the city municipality to accelerate efficient provision of existing and new city services. The main contribution presented in the paper is, indeed, the assessment of the energy savings achieved during the field trials and the study of key performance indicators analyzed during the trial. The paper highlights how FI technologies create the necessary glue and logic that allows the integration of current vertical and isolated city services into a holistic solution. , which enables a huge forward leap for the efficiency and sustainability of our cities. Finally, the trial is a showcase on how added-value services can be seamlessly created on top of the proposed architecture