Total Time Constraint Research Articles

Joint Optimal Data Collection Scheduling and Time Allocation in Full-Duplex WP-IoT Networks

This paper considers a full-duplex wireless powered internet of things (WP-IoT) network, supporting simultaneous wireless power transfer of a hybrid access point in the downlink and collected data transfer of devices in the uplink. Compared to existing works where the data collection process is mostly ignored, the problem of joint data collection scheduling and time allocation is investigated under two objectives, i.e., total throughput maximization subject to a total time constraint and total time minimization subject to minimum collected data constraints. For both objectives, the optimal solution is derived in closed-form. It is revealed that the proposed algorithms outperform various baseline algorithms in existing literature.

Sensing Method for Two-Target Detection in Time-Constrained Vector Gaussian Channel

This paper considers a vector Gaussian channel of fixed identity covariance matrix and binary input signalling as the mean of it. A linear transformation is performed on the vector input signal. The objective is to find the optimal scaling matrix, under the total time constraint, that would: i) maximize the mutual information between the input and output random vectors, ii) maximize the MAP detection. It was found that the two metrics lead to different optimal solutions for our experimental design problem. We have used the Monte Carlo method for our computational work.

Sensing Method for Two-Target Detection in Time-Constrained Vector Poisson Channel

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.

A novel model to eliminate the doubly near‐far problem in wireless powered communication network

In this paper, the newly emerging wireless powered communication network is studied. In doing so, the performance of the global controller (GC) is evaluated, which coordinates the wireless energy transmissions between two sensor nodes. Both the sensors have the same harvested energy for uplink (UL) transmission of information through time-division-multiple-access. Afterwards, the information transmission time is optimised to maximise the common throughput of both the sensors with a total time constraint based on the user's UL channels along with the same harvested energy value. Further, due to the “doubly near-far” phenomenon, a remote sensor from the GC, which has poor channel conditions than a nearer user, has to transmit more time in the UL for maximum common throughput. To overcome this problem, the energy exchange (EEx) model is proposed where both sensors first harvest the same amount of wireless energy and then exchange energy to nullify the different channel conditions between sensors and GC to send their independent information in the UL. Simulation results demonstrate the EEx Model's effectiveness over without energy exchange (WEEx) model in eliminating the doubly near-far problem in wireless powered communication network but at the cost of maximum sum-throughput. The maximum sum-throughput of the proposed EEx model is 35 % lower than the WEEx model. However, the average BER in the proposed EEx model is 74.6 % lower than the WEEx model, which increases the reliability of the model.

Backscatter Communication via Harvest-Then-Transmit Relaying

In backscatter communication (BackCom), the direct link between the tag and the reader is susceptible to the deep shadowing due to the obstruction between them. In this paper, we consider cooperative transmission to relay the tag information. The harvest-then-transmit (HTT) protocol is adopted in the relay, where the relay harvests energy in the first time slot and forwards the tag signal after decoding and re-encoding it. Our objective is to maximize the capacity performance subject to the relay residual energy constraint, the tag circuit energy constraint and the total time constraint by optimizing the power splitting factor and time slots. The resulting optimization problem is shown to be convex, however should be numerically evaluated for optimality. In order to gain more insight and avoid high computational complexity, closed-form expressions are derived for sub-optimal solutions. Simulation results confirm the near-optimal performance of sub-optimal solutions, which are shown to perform much better than traditional schemes.

Throughput Maximization in Wireless Powered Communication Networks

This paper studies the newly emerging wireless powered communication network (WPCN) in which one hybrid access point (H-AP) with constant power supply coordinates the wireless energy/information transmissions to/from distributed users that do not have energy sources. A "harvest-then-transmit" protocol is proposed where all users first harvest the wireless energy broadcast by the H-AP in the downlink (DL) and then send their independent information to the H-AP in the uplink (UL) by time-division-multiple-access (TDMA). First, we study the sum-throughput maximization of all users by jointly optimizing the time allocation for the DL wireless power transfer versus the users' UL information transmissions given a total time constraint based on the users' DL and UL channels as well as their average harvested energy values. By applying convex optimization techniques, we obtain the closed-form expressions for the optimal time allocations to maximize the sum-throughput. Our solution reveals "doubly near-far" phenomenon due to both the DL and UL distance-dependent signal attenuation, where a far user from the H-AP, which receives less wireless energy than a nearer user in the DL, has to transmit with more power in the UL for reliable information transmission. Consequently, the maximum sum-throughput is achieved by allocating substantially more time to the near users than the far users, thus resulting in unfair rate allocation among different users. To overcome this problem, we furthermore propose a new performance metric so-called common-throughput with the additional constraint that all users should be allocated with an equal rate regardless of their distances to the H-AP. We present an efficient algorithm to solve the common-throughput maximization problem. Simulation results demonstrate the effectiveness of the common-throughput approach for solving the new doubly near-far problem in WPCNs.

Two defence applications involving discrete valued optimal control

We present two examples of optimal discrete valued control problems which arise in defence applications. The first one involves the management of batteries in a submarine. In the second example, one wishes to determine an optimal transit path for a submarine through a field of sonar sensors, subject to a total time constraint. A recently developed numerical solution technique for discrete valued optimal control problems is used to solve both of these problems. We present numerical results for each example.

Computational aspects of the optimal transit path problem

We consider a class of path design problems which arise when an object needs to traverse between two points through a specified region. The path must optimise a prescribed criterion such as risk, reliability or cost and satisfy a number of constraints such as total travel time. Problems of this type readily arise in the defence, transport and communication industries. We specifically look at the problem of determining an optimal (in terms of minimizing the overall probability of detection) transit path for a submarine moving through a field of sonar sensors, subject to a total time constraint. A computational strategy along with results are presented.

The first K minimum cost paths in a time-schedule network

The time-constrained shortest path problem is an important generalisation of the classical shortest path problem and in recent years has attracted much research interest. We consider a time-schedule network, where every node in the network has a list of pre-specified departure times and departure from a node may take place only at one of these departure times. The objective of this paper is to find the first K minimum cost simple paths subject to a total time constraint. An efficient polynomial time algorithm is developed. It is also demonstrated that the algorithm can be modified for finding the first K paths for all possible values of total time.

Minimum time paths in a network with mixed time constraints

The time-constrained shortest path problems are generalized from and more realistic than the classical shortest path problem. These problems are most important subproblems in many large-scale real-world problems in the field of transportation. The problem considered in this paper is to find the shortest path in a new kind of time-constrained network, called a mixed time-schedule network, in which departures from some nodes are only allowed at some discrete points. This problem arises in many practical situations. For example, a shipping or transportation from one place to another may be through different kinds of vehicles such as (1) scheduled flights, ocean liners, trains, or buses and (2) rental cars, own cars or own trucks, where the vehicles in (1) have to follow predetermined departure-time schedules and the vehicles in (2) do not have time constraints. The time-constrained shortest path problem is an important generalization of the shortest path problem and has attracted much research interest in recent years. In a recent paper, a new time constraint, namely time-schedule constraint, is introduced. This time constraint assumes that every node in the network has a list of pre-specified departure times and requires that departure from a node take place only at one of these departure times. Therefore, when a time-schedule constraint is considered, the total time in a network includes traveling time and waiting time. In this paper, we consider a network consisting of two types of nodes in terms of their time constraints. The first type of nodes are subject to time-schedule constraints, but the second type is not. For such a network, a set of minimum time (shortest) path problems is studied, including minimization of total time, minimization of total time subject to a total traveling time constraint, minimization of total traveling time subject to a total time constraint and minimization of a weighted sum of total time and total traveling time.

A dual algorithm for the constrained shortest path problem

AbstractIn this paper we develop a Lagrangian relaxation algorithm for the problem of finding a shortest path between two nodes in a network, subject to a knapsack‐type constraint. For example, we may wish to find a minimum cost route subject to a total time constraint in a multimode transportation network. Furthermore, the problem, which is shown to be at least as hard as NP‐complete problems, is generic to a class of problems that arise in the solution of integer linear programs and discrete state/stage deterministic dynamic programs. One approach to solving the problem is to utilize a kth shortest path algorithm, terminating with the first path that satisfies the constraint. This approach is impractical when the terminal value of k is large. Using Lagrangian relaxation we propose a method that is designed to reduce this value of k. Computational results indicate orders of magnitude savings when the approach is applied to large networks.