Demand Vector Research Articles

Data-Driven Robust Taxi Dispatch Under Demand Uncertainties

In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in realtime. They provide knowledge of system models on passenger demand and mobility patterns for efficient taxi dispatch and coordination strategies. Such approaches face new challenges: how to deal with uncertainties of predicted customer demand while fulfilling the system’s performance requirements, including minimizing taxis’ total idle mileage and maintaining service fairness across the whole city; how to formulate a computationally tractable problem. To address this problem, we develop a data-driven robust taxi dispatch framework to consider spatial-temporally correlated demand uncertainties. The robust vehicle dispatch problem we formulate is concave in the uncertain demand and convex in the decision variables. Uncertainty sets of random demand vectors are constructed from data based on theories in hypothesis testing, and provide a desired probabilistic guarantee level for the performance of robust taxi dispatch solutions. We prove equivalent computationally tractable forms of the robust dispatch problem using the minimax theorem and strong duality. Evaluations on four years of taxi trip data for New York City show that by selecting a probabilistic guarantee level at 75%, the average demand–supply ratio error is reduced by 31.7%, and the average total idle driving distance is reduced by 10.13% or about 20 million miles annually, compared with nonrobust dispatch solutions.

Robustness of DC Networks With Controllable Link Weights

We study robustness of dc power networks with controllable link susceptances, or weights , toward balanced disturbances to a nominal supply–demand vector. Consider the set of balanced disturbances, for which there exist feasible link weights so that the resulting link flows are within specified capacity bounds. The margin of robustness is defined as the radius of the largest $\ell _1$ ball around the origin, which belongs to this set. Computation of this margin can be posed as a nonconvex optimization problem. We propose an equivalent multilevel programming approach for a class of networks, in which the nodes with nonzero demand or supply are relatively sparse. This approach is based on recursive application of equivalent bilevel formulation for a relevant class of, possibly nonconvex, optimization problems. The lower level problem in each recursion corresponds to replacing a subnetwork by a (virtual) link with equivalent weight and capacity functions. The equivalent capacity function for a link-reducible network possesses a strong quasi-concavity property. This property facilitates an easy solution to the multilevel programming formulation for tree-reducible networks . The computational gain from such a reduction procedure is illustrated via simulations on a benchmark IEEE network.

On optimal trajectory for the multi-period evolution of FTTx networks

We propose a business/cost model for long-period changes of customer demands in FTTx networks. We show how to exploit this model in the formulation of a MIP optimization problem that, for a given sequence of customer demand vectors provides (when solved to the optimality) the absolute benchmark. The model is relatively complicated and cannot be directly applied to the optimization of real-world networks without a number of algorithmic tricks that are discussed in this paper. The presented approach has been implemented and tested against an example network instance. Test results are given and discussed in the paper.

Performance Optimization of Ad Hoc on Demand Vector Routing Protocol in Cognitive Radio Network

Performance Optimization of Ad Hoc on Demand Vector Routing Protocol in Cognitive Radio Network

On Resource Management in Load-Coupled OFDMA Networks

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations.

Perturbation-minimising frequency assignment to address short term demand fluctuation in cellular network

In cellular network short term demand fluctuation is a very common phenomenon. The demand of any cell may increase or decrease slightly or the system may expand by adding additional cells or the system may shrink if the demands of certain number of cells become zero. In this paper, the perturbation-minimising frequency assignment problem (PMFAP) is considered to address the short term fluctuation in demand vector. PMFAP is a frequency assignment problem in which newly generated demands are satisfied with minimum changes in the already existing frequency assignment keeping all the interference constraints. In this paper, an efficient heuristic algorithm for PMFAP is presented. The efficiency of this algorithm is compared with the existing results from literature. With a slight modification to the proposed algorithm, it can solve the well-known frequency assignment problem (FAP) and its performance is also compared with the existing results using the standard benchmark data sets for FAP.

Modified greedy perimeter stateless routing for vehicular ad hoc networking algorithm

Vehicular ad hoc networking (VANET) can connect vehicles and road side units (RSUs) as self-organised networks. Thus, many routing protocols have been discussed to improve road security and collision avoidance. The purpose of VANET is to improve road safety and find a path in a well-mannered fashion, which is a priority in this field because driving is dangerous and casualties can occur. Many protocols have been proposed in the last several years for VANET. This paper proposes a modified greedy perimeter stateless routing (GPSR) protocol to improve the possibility of finding a path and compares it with other routing protocols such as optimised link state routing (OLSR) and ad hoc on demand vector (AODV).

Perturbation-minimising frequency assignment to address short term demand fluctuation in cellular network

In cellular network short term demand fluctuation is a very common phenomenon. The demand of any cell may increase or decrease slightly or the system may expand by adding additional cells or the system may shrink if the demands of certain number of cells become zero. In this paper, the perturbation-minimising frequency assignment problem (PMFAP) is considered to address the short term fluctuation in demand vector. PMFAP is a frequency assignment problem in which newly generated demands are satisfied with minimum changes in the already existing frequency assignment keeping all the interference constraints. In this paper, an efficient heuristic algorithm for PMFAP is presented. The efficiency of this algorithm is compared with the existing results from literature. With a slight modification to the proposed algorithm, it can solve the well-known frequency assignment problem (FAP) and its performance is also compared with the existing results using the standard benchmark data sets for FAP.

Trust Design: Balancing Smart Contracts Utility and Decentralisation Risk

According to the World Economic Forum, by 2025 10% of global GDP will be stored on blockchains, a type of decentralised database and distributed shared ledger. Smart contracts are automated computable contracts that are executed in blockchains, with the benefit of removing intermediaries and reducing costs. The use cases in finance include: in cross-border payments, to capture obligations, minimize operational errors and expedite transfers; for property and casualty claims in insurance, to automate claims processing through third-party data sources and codification of business rules; for deposits and lending in syndicated loans, to facilitate real-time loan funding and automated servicing activities without intermediaries; for deposits and lending in trade finance, to automate the creation and management of credit facilities ultimately eliminating correspondent banks; for contingent convertible bonds in capital raising, to alert regulators when loan absorption needs to be activated, minimizing need for point-in-time stress tests; for compliance in investment management, to execute reporting and facilitate the automated creation of periodic filings; for proxy voting in investment management, to automate end-to-end confirmation by the validation of votes, increasing transparency; for asset rehypothecation in market provisioning, to enable the real-time reporting of asset history and the enforcement of regulatory constraints, including facilitating clearing and settlement to eliminate need for intermediaries and reduce settlement time; for equity post-trade in market provisioning, to simultaneously transfer equity and cash in real time, reducing the likelihood of errors impacting settlement.The policy implications introduced by decentralization require that economists and lawyers understand this technological shift, and more importantly, the risks related to tangible (e.g consensus selection as a security choice) and intangible (e.g contract incompleteness/code errors) factors. We demonstrate a decision making method where utility is measured by “levels of trust” using artifacts from fields finance applied to a portfolio of institutional smart contract companies. Expected utility is measured by mapping a demand vector field (the attention level), and funding by plotting a scalar field (the investment level); the associated risk exposure is implicit in the consensus mechanism tradeoffs, according to the progression of firms represented in the system of coordinates. The goal is to provide a device for portfolio analysis and construction. The data comes from a panel of 200 million internet users, and investment databases. The result is a comprehensive and scalable view of decentralised portfolios, inspired in the methods of behavioural finance.

Multi-layer Resources Fair Allocation in Big Data with Heterogeneous Demands

The resource fair allocation is very important for the most systems. This paper focuses on the resource allocation in a big data system. This system has the characteristics of multiple resources owning and heterogeneous resource demanding. Firstly, a basic allocation scheme is proposed. Some allocation properties are introduced including sharing incentive, strategy proofness, envy-freeness, Pareto optimality, resource efficiency, single-layer multi-resource fairness, multi-layer single-resource fairness. Secondly, a single layer dominant and max–min fair allocation (SDMMF) is proposed. The basic idea is that the resources are firstly allocated using dominant resource fairness scheme. If some resources are not fully allocated, using max–min fairness scheme allocate the surplus ones. The SDMMF is theoretically proved satisfying all the hierarchical resource allocation properties. Thirdly, a Multi-layer resource allocation scheme, namely multi-layer dominant and max–min fair allocation (MDMMF) is proposed. The basic idea is that a multi-layer resource demand hierarchy can be divided into a series of single layer resource demand. So a resource demand vector can be built in the hierarchy from down to top. Then, the resource allocation of first layer is calculated using SDMMF allocation. The result of such allocation is further allocating in the third layer. Recursively do the MDMMF allocation until the bottom layer nodes obtain resource allocation shares. Lastly, The MDMMF is also theoretically proved satisfying all the hierarchical resource allocation properties.

Design of a proficient hybrid protocol for efficient route discovery and secure data transmission in CEAACK MANETs

Clustering can be made effective in CEAACK MANETs by opting for a cluster head mechanism which ensures that control rests with a single powerful node within a cluster. This cluster head takes on the responsibility of routing the packets efficiently by coordinating with the base station. Generally route maintenance and route discovery is a very important mechanism for maintaining route life. In route discovery, a flooding scheme is usually used to send and receive the route request and route reply message. Based on the route discovery, the routing methods are categorized into Proactive and Reactive methods. Reactive Routing Protocol is also called On Demand Routing Protocol like AODV (AdHoc on Demand Vector Routing) and DSR (Dynamic Source Routing) and they can improve the scalability of CEAACK MANETs by limiting the routing overhead when a new route is requested. However, due to node mobility in CEAACK MANETs, frequent link breakages may lead to frequent path failures and route discoveries, which could increase the overhead of routing protocols and reduce the packet delivery ratio and increases the end-to-end delay.We propose a novel protocol which provides an efficient route discovery technique along with a competent Three Fish algorithm. This is based on understanding the neighbouring clusters and efficiently using this information to reduce the routing overhead in CEAACK MANETs. The proposed protocol is used to determine the forwarding order and exploit the neighbour coverage knowledge more effectively. The novel distributed routing protocol guarantees security, anonymity and high reliability of the established route in a hostile environment, such as an ad hoc wireless network, by encrypting the routing packet header and abstaining from using unreliable intermediate nodes. The major objective of our protocol is to allow trustworthy intermediate nodes to participate in the path construction protocol without jeopardizing the anonymity of the communicating nodes. An enhanced three fish Algorithm is used to ensure the safety of the data during a transmission by using a unique key during the encryption and decryption process. We describe our protocol, STFDR (Secure Three Fish Distributed Routing), and provide its proof of correctness.

Comparison of AODV and Optimized AODV in Vehicular Ad hoc Networks

In this paper, the work is done to identify the need for QoS improvement in VANETs. In vehicular Ad-hoc Network (VANET) a restricted coverage of Wi-Fi and extremely mobile nodes creates the topology highly dynamic and frequently disconnecting. Subsequently, Quality of Service turn out to be an essential issue. Investigation related to work signifies that, an effective routing methodology plays a vital role to improve the QoS parameters. As in opportunistic routing path is not predefined so in normal schemes it leads to packet drop and reduces throughput to overcome these limitations in VANETs, AODV (Ad Hoc on Demand Vector) routing path is optimized by applying a Meta heuristic algorithm – Ant Colony Optimization (ACO). Opportunistic Network Environment (ONE simulator) has been used to carry out the experiment. To measure the performance we used various parameters, such as packet drop, overhead ratio, average latency and the throughput. To create the simulation more realistic a city map has been used in the simulator. Keywords: VANET, ACO, AODV, ONE Simulator, Throughput, Overhead Ratio, Packet Drop, Average Latency

The impact of mobility and node capacity on voice traffic

MANET is one of the wireless networks in which interest is growing due to its flexibility, scalability and non-infrastructure status which is characterized by dynamically distributed clusters of nodes that have the ability to communicate locally and with the Internet. Voice over MANET-IP has become an attractive and popular application. This paper investigates the voice traffic application over the Ad Hoc on Demand Vector reactive routing protocol for different mobility models. The simulation evaluation using OPNET demonstrates the advantage of the Global System for Mobile Communication voice codec over the Internet using the UDP/IP protocol. The voice signaling and the Session Initiation IP (SIP) application layer protocols were tested and compared using different parameters. Different scenarios of mobility and node capacities were considered to test Quality of Service indicators including end-to-end delay, jitter, throughput, load, mean opinion score of voice quality, traffic sent and received, control signals, physical locations of calling nodes and the number of hops. The results showed that using the voice signaling produced better performance in respect of delays when compared to the SIP session. Voice has performed better when compared to the three mobility models with slightly less throughput and longer delays when considering the very fast mobility model. The node capacity showed serious impact on all performance metrics when MANET capacity exceeds 50 nodes in general.

Nash Bargaining Solution Allocation is Not Suitable for Datacenter Jobs

The Nash Bargaining Solution (NBS) has been broadly suggested as an effective solution for the problem of fair allocation of multiple resources, namely bandwidth allocation in datacenters. In spite of being thoroughly studied, and provably strategy-proof for most scenarios, NBS-based allocation methods lack research on the strategic behavior of tenants in the case of proportionality of resource demands, which is common in datacenter workloads. We found that misbehavior is beneficial: by lying about bandwidth demands tenants can improve their allocations. We show that a sequence of selfish improvements leads to trivial demand vectors for all tenants. It essentially removes sharing incentives which are very important for datacenter networks. In this paper, we analytically prove that tenants can misbehave in 2- and 3- tenants cases. We show that misbehavior is possible in one recently proposed NBS-based allocation system if proportionality of demands is taken into account. Monte Carlo simulations were done for 2–15 tenants to show a misbehavior possibility and its impact on aggregated bandwidth. We propose to use another game-theoretic approach, namely Dominant Resource Fairness (DRF) to allocate bandwidth in the case of proportional demands. We show that this method performs significantly better than NBS after misbehavior.

AN EVALUATION OF IMPROVED CLUSTER-BASED ROUTING PROTOCOL IN ADHOC WIRELESS NETWORK

In this paper we presents a performance comparison of Dynamic Source Routing (DSR), Ad hoc On Demand Vector (AODV), Cluster Based Routing Protocol (CBRP) and Improved Cluster Based Routing Protocol (i-CBRP) routing protocols which builds upon a cluster based wireless network infrastructure. This evaluation is necessary due to unique enhancement made to traditional CBRP by way of implementing congestion control scheme as part of the structure establishing i-CBRP. First, we introduce the network infrastructure on which the routing protocols are to send packets. We simulates Mobile Ad-Hoc Network (MANET) network infrastructure using NS2 network simulator. DSR, AODV, CBRP and i-CBRP routing protocols are implemented to generate various simulation results. The results shows that i-CBRP performs better in terms of average packet delay, throughput, volume of traffic controls and average packet delivery ratio when compared with CBRP, while DSR performs better in terms of total volume of control traffic. AODV is the overall best in terms of average packet delay. Key words: Node Density, Evaluation, MANET, Cluster, Routing

An analysis of CO2 emissions in Italy through the Macro Multiplier (MM) approach

The issue of policy design into the environmental economic literature is becoming a crucial point especially for the relevance of its implication on international agreements for climate change and for the definition of climate actions against GHGs emissions. The identification of economic drivers accountable for the CO2 emissions, which represent the major part of GHGs emissions, represents a central topic on literature using Input-Output analysis. The paper proposes a methodological innovation on the study of suitable policy instrument against the raise of CO2emissions, which is based on the approach of the Macro Multipliers (MM) that leads with the recognition of the impact of all those industries responsible for CO2 emissions. From the policy perspective, the relevance of industries responsible for CO2 emissions is also analysed in this approach in which we introduce the target efficiency index and the control effectiveness index across industries. As part of final demand vector, each commodity has its own relevance, or effectiveness, in pursuing the attainment of the target vector. On the other hand as part of the target vectors, each industry emission has its own efficiency in being conveniently modified by changing the policy control vector. The results deriving from the MM approach demonstrate the possibility to overcome the limits of the linkages analysis. In particular, the set of information deriving from the target efficiency and the control effectiveness indices for industries allows designing environmental policies in a framework where economic aggregates are defined in value and physical units. Using the input-output table, this study investigates the impacts of industries activities on CO2 emission using the MM approach for the Italian economy.

Trust Design: Balancing Blockchain Smart Contracts Utility and Decentralisation Risk

According to the World Economic Forum, by 2025 10% of global GDP will be stored on blockchains, a type of decentralised database and distributed shared ledger. Smart contracts are automated computable contracts that are executed in blockchains, with the benefit of removing intermediaries and reducing costs. The use cases in finance include: in cross-border payments, to capture obligations, minimize operational errors and expedite transfers; for property and casualty claims in insurance, to automate claims processing through third-party data sources and codification of business rules; for deposits and lending in syndicated loans, to facilitate real-time loan funding and automated servicing activities without intermediaries; for deposits and lending in trade finance, to automate the creation and management of credit facilities ultimately eliminating correspondent banks; for contingent convertible bonds in capital raising, to alert regulators when loan absorption needs to be activated, minimizing need for point-in-time stress tests; for compliance in investment management, to execute reporting and facilitate the automated creation of periodic filings; for proxy voting in investment management, to automate end-to-end confirmation by the validation of votes, increasing transparency; for asset rehypothecation in market provisioning, to enable the real-time reporting of asset history and the enforcement of regulatory constraints, including facilitating clearing and settlement to eliminate need for intermediaries and reduce settlement time; for equity post-trade in market provisioning, to simultaneously transfer equity and cash in real time, reducing the likelihood of errors impacting settlement.The policy implications introduced by decentralization require that economists and lawyers understand this technological shift, and more importantly, the risks related to tangible (e.g consensus selection as a security choice) and intangible (e.g contract incompleteness/code errors) factors. We demonstrate a decision making method where utility is measured by “levels of trust” using artifacts from fields finance applied to a portfolio of institutional smart contract companies. Expected utility is measured by mapping a demand vector field (the attention level), and funding by plotting a scalar field (the investment level); the associated risk exposure is implicit in the consensus mechanism tradeoffs, according to the progression of firms represented in the system of coordinates. The goal is to provide a device for portfolio analysis and construction. The data comes from a panel of 200 million internet users, and investment databases. The result is a comprehensive and scalable view of decentralised portfolios, inspired in the methods of behavioural finance.

Technology of the Preferences: Linking Consumption Expenditures to Value Added with Minimal Information

The combination of consumption data from expenditure surveys with information contained in IO tables is a crucial step to structural change analysis. Herrendorf et al. (2013) focus the attention on applying a consistent definition of commodities on both the household and production sides — i.e. estimation of utility and production functions — when connecting models with data in any multisector general equilibrium model. The point of departure of these analyses consist, basically, on connecting the information on consumption made by households with the final demand vector (or matrix) present in the IO tables, which is then conveniently modified to produce the multipliers of interest. This process requires the construction of a concordance or bridge matrix to make this connection possible, since several issues affect the combination of these two data sources: differences in price valuation between consumption surveys and IO tables, the influence of taxes and margins or the different product classifications between these two frameworks make this combination a challenge for the researcher. In this paper we explore this challenge with a twofold purpose: (i) to investigate how important a “good” or “bad” conciliation of our consumption data between household surveys and IO tables affect our results in terms of the so-called total requirement matrix or impact analysis; and (ii) to propose a conciliation technique between both data structure, which using only minimal information provides a systematic way or reconciling them if detailed data are not at hand. This technique is based on entropy econometrics and it allows making statistical inference on the bridge matrix estimated. Both research objectives are illustrated by means of numerical simulation and by its application to a real-world case.

Coalition formation based on a task-oriented collaborative ability vector

Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

A Logistical Approach to Managing the Resources of Multi Nomenclature Spare Parts of a Corporate Car Service in Conditions of Risk and Uncertainty of Demand

The task of determining the optimal sizes of spare parts for an auto-service enterprise based on the maximum profit criterion for a discrete distribution of demand is formulated as a problem of quadratic programming with linear constraints. To calculate the probabilistic measure of the distribution of the values of the demand vector components, an approximation is used of the empirical distribution function of the demand components by hyper-Erlanger distribution functions, and the subsequent calculation of the corresponding distribution densities.