Scale-free topology optimization for software-defined wireless sensor networks: A cyber-physical system

Due to the limited resource and vulnerability in wireless sensor networks, maximizing the network lifetime and improving network survivability have become the top priority problem in network topology optimization. This article presents a wireless sensor networks topology optimization model based on complex network theory and cyber-physical systems using software-defined wireless sensor network architecture. The multiple-factor-driven virtual force field and network division–oriented particle swarm algorithm are introduced into the deployment strategy of super-node for the implementation in wireless sensor networks topology initialization, which help to rationally allocate heterogeneous network resources and balance the energy consumption in wireless sensor networks. Furthermore, the preferential attachment scheme guided by corresponding priority of crucial sensors is added into scale-free structure for optimization in topology evolution process and for protection of vulnerable nodes in wireless sensor networks. Software-defined wireless sensor network–based functional architecture is adopted to optimize the network evolution rules and algorithm parameters using information cognition and flow-table configure mode. The theoretical analysis and experimental results demonstrate that the proposed wireless sensor networks topology optimization model possesses both the small-world effect and the scale-free property, which can contribute to extend the lifetime of wireless sensor networks with energy efficiency and improve the robustness of wireless sensor networks with structure invulnerability

Resilient Wireless Sensor Networks Using Topology Control: A Review

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs

Architecture and algorithm for reliable 5G network design

This Ph.D. thesis investigates the resilient and cost-efficient design of both C-RAN and Xhaul architectures. Minimization of network resources as well as reuse of already deployed infrastructure, either based on fiber, wavelength, bandwidth or Processing Units (PU), is investigated and shown to be effective to reduce the overall cost. Moreover, the design of a survivable network against a single node (Baseband Unit hotel (BBU), Centralized/Distributed Unit (CU/DU) or link failure proposed. The novel function location algorithm, which adopts dynamic function chaining in relation to the evolution of the traffic estimation also proposed and showed remarkable improvement in terms of bandwidth saving and multiplexing gain with respect to conventional C-RAN. Finally, the adoption of Ethernet-based fronthaul and the introduction of hybrid switches is pursued to further decrease network cost by increasing optical resource usage

ieee access special section editorial recent advances on radio access and security methods in 5g networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria towards a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In the article by Dao et al. , "Adaptive resource balancing for serviceability maximization in fog radio access networks," the authors propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) is balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH motilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared to the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput

End-to-End Resilience Mechanisms for Network Transport Protocols

The universal reliance on and hence the need for resilience in network communications has been well established. Current transport protocols are designed to provide fixed mechanisms for error remediation (if any), using techniques such as ARQ, and offer little or no adaptability to underlying network conditions, or to different sets of application requirements. The ubiquitous TCP transport protocol makes too many assumptions about underlying layers to provide resilient end-to-end service in all network scenarios, especially those which include significant heterogeneity. Additionally the properties of reliability, performability, availability, dependability, and survivability are not explicitly addressed in the design, so there is no support for resilience. This dissertation presents considerations which must be taken in designing new resilience mechanisms for future transport protocols to meet service requirements in the face of various attacks and challenges. The primary mechanisms addressed include diverse end-to-end paths, and multi-mode operation for changing network conditions

Resilient Service Embedding in IoT Networks

The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency