This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios.

The Deficit Round Robin (DRR) scheduler is widely used in network systems for its simplicity and fairness. However, configuring its integer-valued parameters, known as quanta, to meet stringent delay constraints remains a significant challenge. This paper addresses this issue by demonstrating the convexity of the feasible parameter set for a two-flow DRR system under delay constraints. The analysis is then extended to n-flow systems, uncovering key structural properties that guide parameter selection. Additionally, we propose an optimization method to maximize the number of packets served in a round while satisfying delay constraints. The effectiveness of this approach is validated through numerical simulations, providing a practical framework for enhancing DRR scheduling. These findings offer valuable insights into resource allocation strategies for maintaining Quality of Service (QoS) standards in network slicing environments.

We study service scheduling problems in a slotted system in which agents arrive with service requests according to a Bernoulli process and have to leave within two slots after arrival, service costs are quadratic in service rates, and there is also a waiting cost. We consider fixed waiting costs. We frame the problem as an average cost Markov decision process . While the studied system is a linear system with quadratic costs, it has state dependent control. Moreover, it also possesses a non-standard cost function structure rendering the optimization problem complex. Here, we characterize the optimal policy. We also consider a system in which the agents make scheduling decisions for their respective service requests keeping their own cost in view. We frame this scheduling problem as a stochastic game. Here, we provide Nash equilibrium.

A general communication network has a single Data Center (DC) in its “core”, which serves as a gateway to the Internet. For delay-constrained services of the kind needed by online gaming, this model does not suffice because the propagation delay between the subscriber and the DC may be too high. This requires some servers to be located close to the network edge. Thus, the question of the optimal placement of these edge servers arises. To lower the network design cost, it is also essential to ensure good traffic routing, so that aggregate traffic on each link remains as low as possible. This enables lower capacity assignment on each link and thereby minimizes design cost. In this paper, we study a novel joint optimization problem of network design cost minimization. Edge server placement cost and link capacity assignment cost constitute the total cost. The problem formulated is a large Integer Linear Program (ILP). Unlike others, we provide an exact solution in reasonable time. To achieve this, we apply the Column Generation (CG) technique. The results show a 40% improvement in the design cost, when solved through CG, over other heuristics.

Summary Perpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.

We study service scheduling problems in a slotted system in which jobs arrive according to a Bernoulli process and have to leave within two slots after arrival, service costs are quadratic in service rates, and there is also a linear waiting cost. We illustrate how these systems can be used to model CPU speed scaling, EV charging at a charging center, load scheduling in smart grids etc. We frame the problems as average cost Markov decision processes. While the studied system is a linear system with quadratic costs, it has state dependent control and non-standard cost function structure, rendering the optimization problem complex. We obtain explicit optimal policies in the case when all the jobs are of same size. In particular, we show that the optimal policy is linear or piece-wise linear in the system state, depending on the system parameters. Further, when the job sizes can take two distinct values, we provide an algorithm that yields the optimal policy. We illustrate different forms of these policies via numerical examples.

Campaigners, advertisers, and activists are increasingly turning to social recommendation mechanisms, provided by social media, for promoting their products, services, brands, and even ideas. However, many a time, such social network-based campaigns perform poorly in practice, because the intensity of recommendations drastically reduces beyond a few hops from the source. A natural strategy for maintaining the intensity is to provide incentives. In this paper, we address the problem of minimizing the cost incurred by the campaigner for incentivizing a fraction of individuals in the social network, while ensuring that the campaign message reaches a given expected fraction of individuals. We also address the dual problem of maximizing the campaign penetration for a resource constrained campaigner. To help us understand and solve the above-mentioned problems, we use percolation theory to formally state them as optimization problems. These problems are not amenable to traditional approaches because of a fixed point equation that needs to be solved numerically. However, we use results from reliability theory to establish some key properties of the fixed point, which in turn enables us to solve these problems using algorithms that are linearithmic in maximum node degree. Furthermore, we evaluate the efficacy of the analytical solutions by performing simulations on real-world networks.

We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function-implemented through advertisements-during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin's maximum principle from optimal control theory and solve it numerically using the forward-backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness, and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree-a simple and local measure-performs well on the three social networks used to demonstrate results: 1) scientific collaboration; 2) Slashdot; and 3) Facebook. The optimal strategy targets central nodes when the resource is scarce, but noncentral nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models-that can be modeled using a system of ordinary differential equations-for a network with a known adjacency matrix.

Efficient resource planning in energy harvesting wireless sensor networks (WSNs) is of immense practical significance. However, it is encumbered by the sheer diversity of parameters that affect the performance of such WSNs. In this paper, we propose a unified framework that can be leveraged to efficiently design and deploy such large-scale networks. To this end, we propose an intuitive utility function to quantify the Quality of Monitoring (QoM). Based on this QoM, we formulate a long-term time-averaged joint resource allocation problem, whose optimal solution serves as a metric to compare different deployment scenarios. This resource allocation problem is subject to energy and capacity constraints inherent to WSNs. The capacity constraints, though very intuitive, require us to enumerate all the maximal independent sets in the network-a well known NP-hard problem. Therefore, for computational tractability, we replace the maximal independent set constraints with clique constraints. We also prove the sufficiency of the clique constraints, and present an algorithm that obtains an ϵ-optimal solution to the original problem. Finally, we present numerical evaluations to validate the correctness of the proposed algorithm.

We study the optimal control problem of maximizing the spread of an information epidemic on a social network. Information propagation is modeled as a susceptible-infected (SI) process, and the campaign budget is fixed. Direct recruitment and word-of-mouth incentives are the two strategies to accelerate information spreading (controls). We allow for multiple controls depending on the degree of the nodes/individuals. The solution optimally allocates the scarce resource over the campaign duration and the degree class groups. We study the impact of the degree distribution of the network on the controls and present results for Erdős–Rényi and scale-free networks. Results show that more resource is allocated to high-degree nodes in the case of scale-free networks, but medium-degree nodes in the case of Erdős–Rényi networks. We study the effects of various model parameters on the optimal strategy and quantify the improvement offered by the optimal strategy over the static and bang-bang control strategies. The effect of the time-varying spreading rate on the controls is explored as the interest level of the population in the subject of the campaign may change over time. We show the existence of a solution to the formulated optimal control problem, which has nonlinear isoperimetric constraints, using novel techniques that is general and can be used in other similar optimal control problems. This work may be of interest to political, social awareness, or crowdfunding campaigners and product marketing managers, and with some modifications may be used for mitigating biological epidemics.

We study the optimal control problem of allocating campaigning resources over the campaign duration and degree classes in a social network. Information diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment of susceptible nodes to the infected (informed) class is used as a strategy to accelerate the spread of information. We formulate an optimal control problem for optimizing a net reward function, a linear combination of the reward due to information spread and cost due to application of controls. The time varying resource allocation and seeds for the epidemic are jointly optimized. A problem variation includes a fixed budget constraint. We prove the existence of a solution for the optimal control problem, provide conditions for uniqueness of the solution, and prove some structural results for the controls (e.g., controls are non-increasing functions of time). The solution technique uses Pontryagin's Maximum Principle and the forward-backward sweep algorithm (and its modifications) for numerical computations. Our formulations lead to large optimality systems with up to about 200 differential equations and allow us to study the effect of network topology (Erdos-Rényi/scale-free) on the controls. Results reveal that the allocation of campaigning resources to various degree classes depends not only on the network topology but also on system parameters such as cost/abundance of resources. The optimal strategies lead to significant gains over heuristic strategies for various model parameters. Our modeling approach assumes uncorrelated network, however, we find the approach useful for real networks as well. This work is useful in product advertising, political and crowdfunding campaigns in social networks.

In-network function computation (INFC) is becoming an essential aspect of large database systems where the data is distributed over the network. Such situations arise often in resource constrained wireless sensor network making them a natural candidate for INFC. We study the problem of maximizing the rate of INFC in wireless networks. In this paper, we develop a functional flow model for INFC when the function computation sequence is represented by a directed acyclic graph. We propose a linear program formulation to maximize the rate of INFC over all possible embeddings. We also formulate a mixed integer linear program (MILP) for maximizing the rate of computation for a single embedding on a capacity constrained wireless network. The MILP for finding the single embedding that yields maximum rate turns out to be NP hard; we develop a heuristic to solve this problem by formulating a linear program. We provide numerical results to illustrate the performance of these formulations.

Interference management (RF management) remains one of the main challenges facing the design and deployment of large-scale WLAN. RF management involves the detection, estimation and control of power level, channel allocations and link schedules, to improve the performance of the wireless network. Among interfering links, one can find different types of dependencies. While some of these types can be predicted with relatively low overheads, observing and inferring other types can be a challenging task. In this paper, we ask the question - in WLANs, what is the probability of different types of pair-wise link dependencies? We answer this question by deriving analytical expressions for various types of link dependencies seen in IEEE 802.11 WLANs, numerically evaluating them, and comparing them against simulations.

In this paper, by augmenting beamforming with signal nulling, we present a scheme to improve the equivocation in wireless relay networks. Assuming global channel state information, memoryless adversaries, and the decode-and-forward relaying strategy, we seek to maximize the average achievable secrecy rate between the source and the legitimate destination, subject to an overall power budget per message. Then, exploiting the structure of the optimization problem, we present an online sequential approach to compute an achievable average rate. Finally, we use numerical evaluations to compare our method with the conventional schemes.

In this letter, we propose a scheme to improve the secrecy rate of cooperative networks using Analog Network Coding (ANC). ANC mixes the signals in the air; the desired signal is then separated out, from the mixed signals, at the legitimate receiver using techniques like self interference subtraction and signal nulling, thereby achieving better secrecy rates. Assuming global channel state information, memoryless adversaries and the decode-and-forward strategy, we seek to maximize the average secrecy rate between the source and the destination, subject to an overall power budget. Then, exploiting the structure of the optimization problem, we compute its optimal solution. Finally, we use numerical evaluations to compare our scheme with the conventional approaches.

We present a centralized integrated approach for: 1) enhancing the performance of an IEEE 802.11 infrastructure wireless local area network (WLAN), and 2) managing the access link that connects the WLAN to the Internet. Our approach, which is implemented on a standard Linux platform, and which we call ADvanced Wi-fi Internet Service EnhanceR (ADWISER), is an extension of our previous system WLAN Manager (WM). ADWISER addresses several infrastructure WLAN performance anomalies such as mixed-rate inefficiency, unfair medium sharing between simultaneous TCP uploads and downloads, and inefficient utilization of the Internet access bandwidth when Internet transfers compete with LAN-WLAN transfers, etc. The approach is via centralized queueing and scheduling, using a novel, configurable, cascaded packet queueing and scheduling architecture, with an adaptive service rate. In this paper, we describe the design of ADWISER and report results of extensive experimentation conducted on a hybrid testbed consisting of real end-systems and an emulated WLAN on Qualnet. We also present results from a physical testbed consisting of one access point (AP) and a few end-systems.

Station-Access Point (STA-AP) association is an important function in IEEE 802.11 Wireless LAN (WLAN) management. We obtain an association policy that can be implemented in centralized WLAN management devices, or in STAs, by taking into account explicitly two aspects of practical importance: (a) TCP-controlled short file downloads interspersed with read times (motivated by web browsing), and (b) different STAs associated with an Access Point (AP) at possibly different rates (depending on distance from the AP). Our approach is based on two steps. First, we consider an analytical model to obtain the aggregate AP throughput for long TCP-controlled file downloads when STAs are associated at k different rates r1, r2, ..., rk; this extends earlier work in the literature. Second, we present a 2-node closed queueing network model to approximate the expected average-sized file download time for a user who shares the AP with other users associated at a multiplicity of rates. These analytical results motivate the proposed association policy, called the Estimated Delay based Association (EDA) policy: Associate with the AP at which the expected file download time is the least. Simulations indicate that for a web-browsing type traffic scenario, EDA performs substantially better than other policies that have been proposed earlier. Crucially, the improved performance is sustained even in realistic evaluation scenarios, where the assumptions underpinning the analytical model do not hold. To the best of our knowledge, this is the first work that proposes an association policy tailored specifically for web browsing. Apart from this, our analytical results could be of independent interest.

In this paper, we consider the problem of scheduling in multihop wireless networks subject to interference constraints. We consider a graph based representation of wireless networks, where scheduled links adhere to the K-hop link interference model. We develop a distributed greedy heuristic for this scheduling problem. Further, we show that this distributed greedy heuristic computes the exact same schedule as the centralized greedy heuristic.

This paper describes the implementation of wireless mesh nodes based on the IEEE 802.11s draft where the motivation is to build a real life mesh network. The mesh nodes developed have mesh, mesh access point and mesh portal functionalities simultaneously. The mesh nodes use different radios for mesh and access point functionalities, thus giving better service to client stations. Both reactive and proactive modes of HWMP are supported. The paper also suggests some measures to enhance the performance of the overall network by reducing the number of PREQs.

In this paper, we study the Foschini Miljanic algorithm, which was originally proposed in a static channel environment. We investigate the algorithm in a random channel environment, study its convergence properties and apply the Gerschgorin theorem to derive sufficient conditions for the convergence of the algorithm. We apply the Foschini and Miljanic algorithm to cellular networks and derive sufficient conditions for the convergence of the algorithm in distribution and validate the results with simulations. In cellular networks, the conditions which ensure convergence in distribution can be easily verified.

Network processors today consist of multiple parallel processors (micro engines) with support for multiple threads to exploit packet level parallelism inherent in network workloads. With such concurrency, packet ordering at the output of the network processor cannot be guaranteed. This paper studies the effect of concurrency in network processors on packet ordering. We use a validated Petri net model of a commercial network processor, Intel IXP 2400, to determine the extent of packet reordering for IPv4 forwarding application. Our study indicates that in addition to the parallel processing in the network processor, the allocation scheme for the transmit buffer also adversely impacts packet ordering. In particular, our results reveal that these packet reordering results in a packet retransmission rate of up to 61%. We explore different transmit buffer allocation schemes namely, contiguous, strided, local, and global which reduces the packet retransmission to 24%. We propose an alternative scheme, packet sort, which guarantees complete packet ordering while achieving a throughput of 2.5 Gbps. Further, packet sort outperforms the in-built packet ordering schemes in the IXP processor by up to 35%.

In this paper, we study the performance of client-Access Point (AP) association policies in IEEE 802.11 based WLANs. In many scenarios, clients have a choice of APs with whom they can associate. We are interested in finding association policies which lead to optimal system performance. More specifically, we study the stability of different association policies as a function of the spatial distribution of arriving clients. We find for each policy the range of client arrival rates for which the system is stable. For small networks, we use Lyapunov function methods to formally establish the stability or instability of certain policies in specific scenarios. The RAT heuristic policy introduced in our prior work is shown to have very good stability properties when compared to several other natural policies. We also validate our analytical results by detailed simulation employing the IEEE 802.11 MAC.

The problem of call admission control (CAC) is considered for leaky bucket constrained sessions with deterministic service guarantees (zero loss and finite delay bound) served by a generalized processor sharing scheduler at a single node in the presence of best effort traffic. Based on an optimization process, a CAC algorithm capable of determining the (unique) optimal solution is derived. The derived algorithm is also applicable, under a slight modification, in a system where the best effort traffic is absent and is capable of guaranteeing that if it does not find a solution to the CAC problem, then a solution does not exist. The numerical results indicate that the CAC algorithm can achieve a significant improvement on bandwidth utilization as compared to a (deterministic) effective bandwidth-based CAC scheme.

We consider the problem of call admission and resource management in a code-division multiple-access (CDMA) wireless network supporting several types of services over a range of transmission rates and offering possibly different grades of service. Resource requirements are considered separately for the uplink and downlink. The high-level objective is to design a simple admission scheme that ensures adequate signal-to-interference ratios for both the incoming call (if accepted), as well as previously admitted calls. Our approach is based on two key ideas: 1) an integrated measure of resource utilization that is agnostic to the details of the traffic mix and 2) an estimate of the additional resources required to accommodate the new call seeking admission. The current work considers estimation of the total received power distribution on the uplink and the total transmitted power distribution on the downlink, and prediction of their displacements as a result of admitting a new call. The total received/transmitted power distributions are estimated based on data obtained from the power control module. The displacement of total received/transmitted power is predicted based on the characteristics of the incoming call and the current resource utilization. Dynamic call capacities are compared with static capacities to indicate the effectiveness of the proposed algorithm in achieving high network utilization with low probability of overload.

We consider the problem of on-line routing of bandwidth-guaranteed virtual paths. The basic idea is to route virtual paths such that enough spare capacity is left for accommodating weighted additional demands simultaneously between all other source-destination pairs. The problem formulation results in an integer linear program (ILP), the solution of which gives the optimal routing. However solving the ILP is NP-hard and so we resort to heuristics. We assign weights to the links in the network and then use shortest path routing. The novel aspect of the weight assignment is that the weights take into account the residual link capacities, as well as the number of source-destination pairs that could potentially use the link. Simulations show that the proposed algorithm performs better than existing algorithms in the literature (e.g. MIRA) for a variety of network topologies and traffic scenarios.

We investigate the performance of routing and rate allocation (RRA) algorithms in rate-based multi-class networks, in particular, a network of routers that support the IntServ guaranteed services. We measure the performance of an RRA algorithm in terms of its weighted carried traffic. We derive an upper bound on the weighted carried traffic of any RRA algorithm. The bound can be computed by linear programming. Moreover we show that a fixed RRA algorithm achieves the bound asymptotically. Therefore the bound can be used as an absolute benchmark for the performance of various RRA algorithms. We illustrate this by comparing the simulated performance of a simple shortest-path RRA (SP-RRA) algorithm with the bound.

In wireless mobile networks, loss of packets is mostly because of corruption of data on the wireless link. TCP responds to these losses as if they are due to congestion, and this results in poor throughput. We suggest enhancements to TCP that, if implemented, will help the receiver in separately identifying corruption losses and congestion losses. Based on this information, the receiver will generate Explicit Loss Notification (ELN) for the sender. We derive an expression for the probability of a receiver generating successful ELN, assuming a generic link layer protocol that is used for data transfer over wireless links. We also suggest modifications to the sender behavior on receiving successful ELN from the receiver. We compare the proposed scheme (called "NewRenoEln") and the standard "NewReno" TCP via simulation, and find considerable improvement in data throughput over wireless links.

In this paper, we investigate the problem of providing worst-case end-to-end delay guarantees to a token bucket constrained flow traversing a series of N packet schedulers. We consider a network of routers that support the guaranteed service class of the IETF Integrated Services (IntServ) Working Group; this service class is proposed to provide quality of service (QoS) in the Internet. Under this framework, a worst-case end-to-end delay bound to a flow is provided by allocating a rate at each network element on the path of the flow. We associate a cost with allocating a rate at each link. The cost is assumed to be a convex and non-decreasing function of rate. We investigate the problem of obtaining an optimal rate allocation for a flow that minimizes the total cost subject to the delay requirement and the available link capacity constraints. Allocating an identical rate at each link on the path of a flow is a widely used approach under the guaranteed service framework. We investigate the optimality of this approach and show that under certain conditions, it need not be optimal. Moreover, we investigate the optimal solution to the total cost minimization problem and give scenarios in which we can explicitly obtain the optimal solution. Based on these results, we present an algorithm for optimal rate allocation that is based on multiple rates. However, with blocking probability as the performance criterion, we find through simulations, that the optimal rate allocation algorithm is only marginally better for connections with longer path lengths at the expense of those with shorter path lengths. We also observe that the performances of both the algorithms are very close and so, in practice, the simpler identical rate algorithm may be sufficient.

Generalized processor sharing (GPS) is an idealized fluid discipline with a number of desirable properties. Its packetized version PGPS is considered to be a good choice is a packet scheduling discipline to guarantee quality-of-service in IP and ATM networks. The existing connection admission control (CAC) frameworks for GPS result in a conservative resource allocation. We propose an optimal CAC algorithm for a GPS scheduler, for leaky-bucket constrained connections with deterministic delay guarantees. Our numerical results show that the optimal CAC results in a higher network utilization than the existing CACs.

In a conference, considering the packets only from a set of selected clients can reduce the degradation of the quality of speech because mixing packets from all clients can lead to lack of clarity in the speech of any participants. The automatic selection should be smooth and should not cause frequent interruptions. A method of selecting the clients for mixing is suggested here based on a new quantifier of the voice activity called loudness number (LN). The dependence of the loudness number on the amplitude of the packet at present and the past activity is clearly brought out. The structure of the packet used has been explained. A method to avoid echo and enhance the quality of the conference is presented. The contributions of the paper are expected to aid in the implementation of H.323 recommendations for the multipoint processors (MP). A working prototype based on the proposed loudness number is already functional.

We consider the problem of admitting calls to a CDMA wireless network supporting stream and packet services over a range of transmission rates and offering possibly different grades of service. Resource requirements are considered separately for the uplink and downlink. The high-level objective is to design a simple admission scheme for the uplink that ensures adequate signal-to-interference ratios for both the incoming call (if accepted), as well as previously admitted calls. Our approach is based on two key ideas: (a) a measure of resource utilization that is independent of how resources are allocated among the ongoing calls, and (b) an estimate of the additional resources required because of the new call seeking admission. The current work considers measurement of the received interference power distribution and estimation of its displacement as a result of admitting a new call. The interference distribution is obtained from the power control module. The interference displacement is estimated based on the characteristics of the incoming call and the current resource utilization. Dynamic call capacities are compared with static capacities to indicate the effectiveness of the algorithm in achieving high network utilization with low probability of overload.

Multicast is an efficient paradigm for transmitting data from a sender to a group of receivers. In this paper, we focus on multicast in single channel multi-access wireless local area networks (LANs) comprising several small cells. In such a system, a receiver cannot correctly receive a packet if two or more packets are sent to it at the same time, because the packets "collide". Therefore, one has to ensure that only one node sends at a time. We look at two important issues. First, we consider the problem of the sender acquiring the multi-access channel for multicast transmission. Second, for reliable multicast in each cell of the wireless LAN, we examine ARQ-based approaches. The second issue is important because the wireless link error rates can be very high. We present a new approach to overcome the problem of feedback collision in single channel multi-access wireless LANs, both for the purpose of acquiring the channel and for reliability. Our approach involves the election of one of the multicast group members (receivers) as a "leader" or representative for the purpose of sending feedback to the sender. For reliable multicast, on erroneous reception of a packet, the leader does not send an acknowledgement, prompting a retransmission. On erroneous reception of the packet at receivers other than the leader, our protocol allows negative acknowledgements from these receivers to collide with the acknowledgement from the leader thus destroying the acknowledgement and prompting the sender to retransmit the packet. Using analytical models, we demonstrate that the leader-based protocol exhibits higher throughput in comparison to two other protocols which use traditional delayed feedback-based probabilistic methods. Last, we present a simple scheme for leader election.