Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks

Abstract

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

Network Slicing Resource Mapping Algorithm Oriented to Delay and Reliability Awareness

“…b. Long-term Average Revenue to Cost Ratio: First, after a request is successfully deployed at time T, its revenue is defined as in (10), and its cost is defined as in (11). Where () v cpu n represents the CPU resource constraint required by the virtual node v n in the request, () v bw l represents the bandwidth resource constraint required by the virtual link v l in the request, and () v Ml represents the underlying physical path that maps the virtual link.…”

“…First, in the existing delay-aware research, the authors of [8][9][10] assign the delay as a resource to the underlying link, and set the algorithm optimization goal to minimize link delay resources to meet low-latency requirements slicing business request. The authors of the literature [11,12] transformed the optimization goal of minimizing the delay into the topology goal of the minimum number of hops in the path, and achieved the purpose of reducing the delay by reducing the node size and the number of transmission hops in the path. Secondly, in the current research on reliability perception, the literature [13][14][15][16][17] improves the reliability of the network by setting backup.…”

Resource allocation for dataflow applications in FANETs using anypath routing

“…Despite the primary focus of WVNE is link assignment, some research works consider node properties in wireless scenarios. In [34] the selection of nodes is based on the cost of all potential choices in the substrate network, and [35] proposes an algorithm that makes the election according to the cost of executing a task in a node that has a certain degree of failure and the deployed backup entities that may recover it.…”

In-depth analysis and open challenges of Mist Computing

“…For that purpose, virtual network embedding (VNE) techniques are used to automate this process and optimize the assignment in each setting. New algorithms must be suggested that fit the special features of constrained devices and guarantee good quality of service in smart perception networks [94]. • Privacy: Mist Computing gives the opportunity to change the present model of sharing and storing data according to privacy policies, which was established by the Cloud, i.e., gathering all information in an external centralized organization.…”