Heterogeneous Resource Allocation Algorithm for Ad Hoc Networks with Utility Fairness

Abstract

Resource allocation is expected to be a most important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented for ad hoc networks, supporting both elastic and inelastic traffic. First, by combining the first order Lagrangian method with pseudo utility, the original nonconvex problem is converted into a new convex one. Then, we successfully solve the heterogeneous problem with the dual-based decomposition approach. In addition, we integrate utility fairness into the resource allocation framework, which can adaptively manage the tradeoff between elastic and inelastic flows. Simulations show and prove that HRA converges fast and can achieve the global optimum starting from many different network conditions, such as elastic, inelastic, and hybrid scenario. With both considerations of flow rate and utility fairness, HRA improves the overall network utility and system throughput greatly.

Priority Service Provisioning and Max–Min Fairness: A Utility-Based Flow Control Approach

“…When the algorithm reaches a stable state, denoted as (17) At the stable state, the associated utility U s of source s is equal to…”

“…Their max-min fair flow control algorithms are hence impractical from an engineering point of view. Furthermore, in order to cope with different users with different QoS requirements, a new criterion named utility max-min fairness is initially proposed in [15] and later on comes various bandwidth allocation algorithms [16,17]. Particularly in [15], the links require the information of utility functions from all the traversed sources, which makes network implementation difficult.…”

Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation

“…As an alternative of [21], a bio-inspired method using multihop desynchronization algorithm with time division multiple access has been projected [22], which is entirely based on resource allocation in distributed networks. is type of algorithm is able to calculate data transmission rate that attempts to act as reference source for allocating resources in addition to collision detection.…”

“…is type of algorithm is able to calculate data transmission rate that attempts to act as reference source for allocating resources in addition to collision detection. By implementing [22], the problem on hidden nodes in multihop networks is resolved effectively by sharing resources with nodes that are located adjacently. Identical to [21,22], for providing good performance, a resource-aware task scheduling method is implemented [23] for exploiting weight exponentially with exploitation to track all relevant applications on WSNs.…”

“…By implementing [22], the problem on hidden nodes in multihop networks is resolved effectively by sharing resources with nodes that are located adjacently. Identical to [21,22], for providing good performance, a resource-aware task scheduling method is implemented [23] for exploiting weight exponentially with exploitation to track all relevant applications on WSNs. e algorithm integrated in [23] will also track the quality and consumption of energy by using some learning techniques.…”