Task Allocation Protocol Research Articles

A Genetic Algorithm for Task Allocation Problem in the Internet of Things

In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical definition for network lifetime in the IoT is to increase the period of cooperation between objects to carry out all the assigned tasks. The main contribution in this paper is to address the problem of task allocation in the IoT as an optimization problem with a lifetime-aware model. A genetic algorithm is proposed as a task allocation protocol. For the proposed algorithm, a problem-tailored individual representation and a modified uniform crossover are designed. Further, the individual initialization and perturbation operators (crossover and mutation) are designed so as to remedy the infeasibility of any solution located or reached by the proposed genetic algorithm. The results showed reasonable performance for the proposed genetic-based task allocation protocol. Further, the results prove the necessity for designing problem-specific operators instead of adopting the canonical counterparts.

A New Task Allocation Protocol for Extending Stability and Operational Periods in Internet of Things

Internet of Things (IoT) is a groundbreaking technology which recently enjoyed a large increase in interest as it connects both digital and physical worlds together and allowing different devices with various capabilities to share their resources to perform a demanded task. Most of IoT objects are heterogeneous by their nature and are supplied with limited battery life. Therefore, distributing tasks among these objects is a big challenge. One of the primary objectives of task allocation algorithms is minimizing the energy consumption while maximizing the task allocation reliability. In this paper, we address the problem of task allocation in IoT as an optimization problem. We develop a nature-inspired meta-heuristic algorithm with heterogeneity aware heuristics to tackle the formulated problem. The proposed algorithm aims to ensure reliable task allocation while boosting the energy efficiency and the stability period of the network. Extensive experimental analysis of the proposed algorithm is realized using MATLAB and OMNET++ simulation scenarios. The results are compared with the most relevant algorithm in the literature and the superiority of the proposed algorithm in terms of energy efficiency has been proved.

Equivalence class verification of the contract net protocol-extension

The contract net protocol is a task allocation protocol that facilitates negotiation between bidders and an auctioneer in a multi-agent system to form a contract. The contract net protocol-extension (CNP-ext) allows the bidders to interact with more than one auctioneer concurrently, and to update their bids until a bid is granted. This introduces flexibility and ensures better selection of a bid. In earlier work, we modelled the CNP-ext with coloured petri nets and showed that it terminates correctly. We also analysed the terminal states and proved that the agents have consistent beliefs at the end of negotiations. Lastly, we showed how the number of terminal states and channel bounds are related to the number of bidders. However, due to state space explosion, our results were restricted to five bidders. As all bidders are considered identical, symmetries exist in the state space. Hence, we decided to tackle this problem using equivalence classes. In this paper, after first presenting an introduction to the CNP-ext, we present a substantial revision to our CNP-ext model and an intentionally didactic description of its operation. Then, we use equivalence classes to analyse the protocol and, importantly, present a proof that our equivalence specification is consistent. Using this technique, we are able to extend the verification results to twenty-four bidders.

Collaborator: A Nonholonomic Multiagent Team for Tasks in a Dynamic Environment

In our previous work, we proposed a potential field-based hybrid path planning scheme for robot navigation that achieves complete coverage in various tasks. This paper is an extension of this work producing a multiagent framework, Collaborator, that integrates a high-level negotiation-based task allocation protocol with a low-level path planning method taking into consideration several real-world robot limitations such as nonholonomic constraints. Specifically, the proposed framework focuses on a class of complex motion planning problems in which robots need to cover the whole workspace, coordinate the accomplishment of a task, and dynamically change their roles to best fit the task. Applications in this class of problems include bomb detection and removal as well as rescuing of survivors from accidents or disasters. We have tested the framework in simulations of several tasks and have shown that Collaborator can satisfy nonholonomic constraints, cooperatively accomplish given tasks in an initially unknown dynamic environment while avoiding collision with other team members. Finally we prove that the proposed control laws are stable using the Lyapunov stability theory.

An Extended Multi-Agent Negotiation Protocol

This article presents a task allocation protocol that is efficient in time and tolerates crash failures in multi-agent systems. The protocol is an extension of the negotiation protocol defined by Smith and Davis [25, 26] for task allocation. Our extension of the Contract Net Protocol (1) enables an agent to manage several negotiation processes in parallel; (2) optimizes the length of the negotiation processes among agents; (3) reduces the contractors' decommitment situations; (4) enables the detection of failures of an agent participating in a negotiation process and prevents a negotiation process with blocked agents.