Distributed multi-stage image compression—where visual content traverses multiple processing nodes under varying quality requirements—poses challenges. Progressive methods enable bitstream truncation but underutilize available compute resources; successive compression repeats costly pixel-domain operations and suffers cumulative quality loss and inefficiency; fixed-parameter models lack post-encoding flexibility. In this work, we developed the Hierarchical Cascade Framework (HCF) that achieves high rate-distortion performance and better computational efficiency through direct latent-space transformations across network nodes in distributed multi-stage image compression systems. Under HCF, we introduced policy-driven quantization control to optimize rate–distortion trade-offs, and established the edge quantization principle through differential entropy analysis. The configuration based on this principle demonstrates up to 0.6dB PSNR gains over other configurations. When comprehensively evaluated on the Kodak, CLIC, and CLIC2020-mobile datasets, HCF outperforms successive-compression methods by up to 5.56% BD-Rate in PSNR on CLIC, while saving up to 97.8% FLOPs, 96.5% GPU memory, and 90.0% execution time. It also outperforms state-of-the-art progressive compression methods by up to 12.64% BD-Rate on Kodak and enables retraining-free cross-quality adaptation with 7.13-10.87% BD-Rate reductions on CLIC2020-mobile.

We present a practical in-network compression system for multimedia delivery under dynamic wireless conditions at the network edge. Our system leverages Dynamic Multi-Level AutoEncoder (DMAE), allowing edge nodes to apply additional compression in response to local congestion. This enables runtime, feedback-free adaptive compression, ensuring timely delivery of multimedia traffic. We demonstrate the system's feasibility on a programmable three-node testbed.

As Wi-Fi access points (APs) are deployed more densely, the cross-technology interference (CTI) from Wi-Fi is becoming a real threat to co-existing low-power IoT protocols such as BLE. In this work, we pay attention to Wi-Fi beacons that take a substantial amount of airtime in the 2.4 GHz Industrial Scientific Medical (ISM) band. We develop B-hop, a standard-compliant framework that adjusts BLE frequency hopping to avoid expected Wi-Fi beacons in the time domain. B-hop operates on BLE master devices where Wi-Fi and BLE protocols are co-located, and it requires no modifications to Wi-Fi devices or BLE slave devices. B-hop predicts future beacon transmissions and reschedules the time domain of frequency hopping to avoid collisions. We implement a B-hop prototype using Zephyr, an open-source embedded operating system that allows us to modify necessary BLE link-layer operations. We evaluate the performance of B-hop through simulations and test-bed experiments, and demonstrate that B-hop significantly outperforms legacy BLE frequency hopping schemes that only use frequency-domain adjustment.

In this work, we perform a comparative study of centralized and decentralized update strategies for the basic remote tracking problem of many distributed users/devices with randomly evolving states. Our goal is to reveal the impact of the fundamentally different tradeoffs that exist between information accuracy and communication cost under these two update paradigms. In one extreme, decentralized updates are triggered by distributed users/transmitters based on exact local state-information, but also at a higher cost due to the need for uncoordinated multi-user communication. In the other extreme, centralized updates are triggered by the common tracker/receiver based on estimated global state-information, but also at a lower cost due to the capability of coordinated multi-user communication. We use a generic superlinear function to model the communication cost with respect to the number of simultaneous updates for multiple sources. We characterize the conditions under which transmitter-driven decentralized update policies outperform their receiver-driven centralized counterparts for symmetric sources, and vice versa. Further, we extend the results to a scenario where system parameters are unknown and develop learning-based update policies that asymptotically achieve the minimum cost levels attained by the optimal policies.

We investigate a remote estimation system with communication cost for multiple Internet-of-Things sensors, in which the state of each sensor changes according to a Wiener process. Under sublinear communication cost structure, in which the per-transmission cost decreases with the number of simultaneous transmissions, we address an interesting unexplored trade-off under source dynamics between frequent updates of a smaller number of sensors at a higher cost and sporadic updates of a larger number of sensors at a lower cost. We first suggest two benchmark strategies, an all-at-once policy and a multi-threshold policy, and generalize them to a unified framework, called the MAX-$k$policy. Furthermore, we address the problem of parameter optimization of the MAX-$k$policy by developing online learning algorithms with stochastic feedback and a continuous search space. Through simulations, we demonstrate that the joint solution of the MAX-$k$policy and particle swarm optimization-based online learning achieves a high performance, outperforming the well-known upper confidence bound-based competitor.

Vehicles on roads have increasingly powerful computing capabilities and edge nodes are being widely deployed. They can work together to provide computing services for onboard driving systems, passengers, and pedestrians. Typical applications in vehicular systems have service requirements such as low latency and high reliability. Most studies in vehicular networks concerning latency and reliability focus on vehicular communication at the network level. Based on these fundamental works, an increasing proportion of vehicles boast complex applications that require service-level end-to-end performance guarantees. Several works guarantee service-level latency or reliability while new and innovative applications are demanding a joint optimization of the above two metrics. To address the critical challenges induced by the joint modeling of latency and reliability, system uncertainty, and performance and cost trade-off, we employ service request duplication to ensure both latency and reliability performance at the service level. We propose an online learning-based service request duplication algorithm based on a multi-armed bandit framework and Lyapunov optimization theory. The proposed algorithm achieves an upper-bounded regret compared to the oracle algorithm. Simulations are based on real-world datasets and the results demonstrate that the proposed algorithm outperforms the benchmarks.

In this work, we develop an agent-based model to predict the infection spread with accuracy. Under COVID-19 pandemic, we faced difficult decision-making of disease control including social distancing and shutdown, which significantly restricted our daily living. Without strong evidences to its effect, however, it was very hard to implement necessary policies in a timely manner. To this end, it is imperative to design a computationally efficient simulation model that can predict the infection spread with accuracy, taking into account the changes of important control policies. We develop an agent-based model that can incorporate individual behaviors and interactions, while capturing large-scale features of the infection spread. We verify the accuracy of our model by comparing the prediction results with the traces of COVID-19 confirmed cases in several countries.

Content delivery networks (CDNs) have been providing the key engineering and economic mediation between content providers (CPs) and Internet service providers (ISPs) in content delivery over the Internet. They significantly improve the quality of service experience for today’s Internet traffic. We model the CDN as a business-to-business platform that provides caching and other services in the Internet content delivery chain between the CPs and the ISPs. The CPs and ISPs that subscribe to the CDN receive a traffic boost relative to their base traffic and in return the CDN prescribes a subscription charge to the CPs, and to the ISPs. We assume that the CDN provides its service without price or quality differentiation between the CPs (or between the ISPs). In this setting we analyze the revenue maximizing prices of the CDN on the two sides of the platform, and its effect on the connection structure on the two sides. We first consider the oligopoly model, where we formulate a full information, leader-follower game. The CDN is the leader and sets the subscription prices for the CPs and the ISPs. The ISPs and CPs are the followers and they make the binary decision of subscribing to the CDN or not. We extend this model to a retail CP market, where the CDN determines the revenue maximizing price using a heuristic and the CPs make the subscription decision. Using extensive numerical analyses, we show that the CDN will always provide sufficient resources to the CPs and that the revenue maximizing price will essentially price out the CPs and ISPs that have a low monetization capability.

In this work, we perform a comparative study of centralized and decentralized update strategies for the basic remote tracking problem of many distributed users/devices with randomly evolving states. Our goal is to reveal the impact of the fundamentally different tradeoffs that exist between information accuracy and communication cost under these two update paradigms. In one extreme, decentralized updates are triggered by distributed users/transmitters based on exact local state-information, but also at a higher cost due to the need for uncoordinated multi-user communication. In the other extreme, centralized updates are triggered by the common tracker/receiver based on estimated global state-information, but also at a lower cost due to the capability of coordinated multi-user communication. We use a generic superlinear function to model the communication cost with respect to the number of simultaneous updates for multiple sources. We characterize the conditions under which transmitter-driven decentralized update policies outperform their receiver-driven centralized counterparts for symmetric sources, and vice versa. Further, we extend the results to a scenario where system parameters are unknown and develop learning-based update policies that asymptotically achieve the minimum cost levels attained by the optimal policies.

Wi-Fi scanning that searches neighboring access points (APs) is an essential prerequisite for Wi-Fi operations such as initial association and handover. As the traffic demand increases, APs are more densely deployed and the number of operating Wi-Fi channels also increases, which, however, results in additional scanning delay and makes the scanning a burdensome task. In this paper, we note that the co-location of Wi-Fi protocol with BLE protocol is a common practice in IoT networks, and develop a Wi-Fi passive scanning framework that uses BLE to assist scanning. Although the framework has great potential to improve scanning performance without explicit message exchanges, there are technical challenges related to time synchronization and channel switching delay. We address the challenges and develop a practical passive scanning scheme, named BLESS-Sync. We verify its performance through testbed experiments and extensive simulations, and show that BLESS-Sync significantly outperforms legacy Wi-Fi scanning in terms of scanning delay and energy efficiency.

In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of$O(N)$and$O(NK)$, respectively, where$N$denotes the number of users and$K$denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.

The Internet of symmetric traffic flows between networks and a hierarchical topology, has long given way to one with significantly asymmetric traffic flows and a flatter topology. The Internet topology of today may be characterized as having three key types of networks—content providers, user access providers, and transit providers. In this Internet, best-effort routing of centrally stored content using distributed protocols has been seen to be inadequate to provide a suitably reliable transport service with the requisite quality of service to the end user. Two important developments that mitigate this gap in the capability of the traditional Internet and the needs of modern content are (i) direct peering arrangements between content networks and ISPs, and (ii) widespread use of content distribution networks (CDNs), who also peer with ISPs. In this paper we first analyze the economics of such peering arrangements. Using microeconomic models from the industrial organization literature, we first develop the conditions for a content provider to connect directly to a service provider, possibly via a peering link. Further, when such a direct link is indeed sought, we analyze the quality of the link vis-a-vis the default option of using a transit service. We then extend our results to the case of CDN, and analyze the content provider market coverage by CDNs. Finally, we discuss the implications of these results on the objectives sought by net neutrality regimes.

The explosion of mobile traffic volume has led to dense deployment of IEEE 802.11 WLANs. As a consequence, periodic beacon transmissions can overwhelm the air-time, leading to significant air-time depletion for data transmissions. In this work, we develop an opportunistic air-time sharing scheme, named BeaconRider, that facilitates simultaneous data and beacon transmissions aimed at improving spectrum efficiency in dense network environments. The proposed method works for downlink communication and allows access points (APs) to coordinate with each other in a distributed manner to exploit opportunities provided by the capture effect. Our protocol is backward compatible with legacy 802.11 APs. Through experiments with a prototype implementation using off-the-shelf IEEE 802.11n dongles as well as extensive ns-3 simulation, we show that the proposed method achieves substantial performance gains that increase with the number of APs.

Bluetooth and Wi-Fi are the most widely used wireless technologies because they use unlicensed spectrum and are widely deployed on the latest mobile devices. For seamless Wi-Fi connectivity in mobile environments, the mobile device should maintain the information of adjacent access points (APs) through the scanning procedure, which often consumes a significant amount of energy and time. In this paper, we develop SplitScan that enables mobile devices to share Wi-Fi scanning information with adjacent stations (STAs) via Bluetooth packet exchange. We evaluate its performance through experiment with a testbed implementation as well as extensive simulation. The results show that SplitScan saves vonsiderable energy and time during the Wi-Fi scanning process.

In Cognitive Radio Networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utility. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. A reward of a channel is stochastic with unknown distribution, and statistically different for each user. Each user either explores a channel to learn the channel statistics, or exploits the channel with the highest expected reward based on information collected so far. Further, a channel should be accessed exclusively by one user at a time due to a collision. Using multi-armed bandit framework, we develop a provably efficient solution whose computational complexity is linear to the number of users and channels.

The coexistence problem of different wireless protocols that share a common frequency spectrum has attracted much attention owing to the proliferation of heterogeneous wireless networks in the research community. Recently, Bluetooth (BT) and Wireless LAN (WLAN) protocol stacks have been integrated as a single-chip communication module, and they now even share the antenna as well as the spectrum. In this paper, we show that this integration provides new opportunity for one protocol to better understand the other and to operate in harmony to avoid mutual interference. We develop an Opportunistic Bluetooth Transmission (OBT) scheme that enables a dual stack device having an integrated module to exploit previously-unused deferring times of the WLAN protocol. We evaluate its performance through not only model-based analysis but also practical implementation in a prototype testbed. The results show that the OBT scheme can significantly improve throughput of the dual-stack device.

Cognitive Radio (CR) networks have received significant attention as a promising approach to improve the spectrum efficiency of current license-based regulatory system. In CR networks, a Secondary User (SU) can use a spectrum vacancy that can be detected by either sensing-before-transmission or database access. However, it is often difficult to detect a vacant spectrum opportunity because of inaccuracies due to sensing and delays to update and/or the database that holds this information. In this paper, we develop a hybrid detection framework in multi-channel CR networks, where an SU can selectively sense a channel for spectrum vacancy by accessing the spectrum history of Markovian channels. We focus on the value of the channel history information offered by the Primary Provider (PP) of each channel, and consider a market for the information exchange between multiple PPs and SUs. We investigate the interplay between of the PPs and the SUs through their pricing and buying decisions for this information, in the presence of sensing inaccuracy, i.e., false alarm and miss detection.

We consider the problem of scheduling in wireless networks with the aim of maintaining up-to-date and synchronized (also called, aligned) information at the receiver across multiple flows. This is in contrast to the more conventional approach of scheduling for optimizing long-term performance metrics such as throughput, fairness, or average delay. Maintaining the age of information at a low and roughly equal level is particularly important for distributed cyber-physical systems, in which the effectiveness of the control decisions depends critically on the freshness and synchrony of information from multiple sources/sensors. In this paper, we first expose the weakness of several popular MaxWeight scheduling solutions that utilize queue-length, delay, and age information as their weights. Then, we develop a novel age-based scheduler that combines age with the interarrival times of incoming packets in its decisions, which yields significant gains in the information freshness at the receiver. We characterize the performance of our strategy through a heavy-traffic analysis that establishes upper and lower bounds on the freshness of system information.

We consider a cognitive radio network, where primary users have priority over the spectrum resources, and secondary users can exploit the unused resources through channel sensing. Due to sensing inaccuracy, the secondary traffic may obstruct the primary traffic. A penalty for collision has been used to protect the primary traffic, which is often designed to provide a fixed per-collision compensation or to restrict the collision rate at an acceptable level. In this work, we develop a framework that can protect the primary traffic taking into account the Quality of Service of the primary traffic. In particular, we pay attention to the delay performance, which is determined not only by the collision rate but also by the amount of traffic in both networks. We design a novel model with coupled queues, and successfully incorporate dynamic interactions between the two systems through the standard optimization problem. We also consider the practical requirement of no direct sharing of the system information between the two networks, and develop a close-to-optimal solution of per-collision price and channel sensing under mild assumptions. We evaluate its performance through simulations.

We consider the problem of scheduling in wireless networks with the aim of maintaining up-to-date and synchronized (also called, aligned) information at the receiver across multiple flows. This is in contrast to the more conventional approach of scheduling for optimizing long-term performance metrics such as throughput, fairness, or average delay. Maintaining the age of information at a low and roughly equal level is particularly important for distributed cyber-physical systems, in which the effectiveness of the control decisions depends critically on the freshness and synchrony of information from multiple sources/sensors. In this work, we first expose the weakness of several popular MaxWeight scheduling solutions that utilize queue-length, delay, and age information as their weights. Then, we develop a novel age-based scheduler that combines age with the interarrival times of incoming packets in its decisions, which yields significant gains in the information freshness at the receiver. We characterize the performance of our strategy through a heavy-traffic analysis that establishes upper and lower bounds on the freshness of system information.

Most distributed wireless scheduling schemes that are provably efficient have been developed under the protocol model, which describes interference constraints in a binary form. However, the oversimplified interference model imposes fundamental limitations on the performance in practice. The signal-to-interference-plus-noise-ratio (SINR) based interference model is more accurate and realistic accounting for the cumulative nature of the interference signals, but its complex structure makes the design of scheduling schemes much more challenging. In this paper, we focus on the scheduling performance under the SINR model and develop random access scheduling schemes that are amenable to implement in a distributed fashion with only local information. We analytically show that they are provably efficient under the SINR model, and through simulations demonstrate that they empirically perform better than the theoretical performance bound.

In this paper, we propose a scalable broadcast protocol, named Subcarrier-level Tone-signal based Broadcast (ST-BCAST), that disseminates a packet over OFDM-based wireless multi-hop networks in an efficient and reliable manner. Exploiting collision-resilient tone-signals and receiver-triggered forwarding decision/cancellation, ST-BCAST achieves both high packet delivery ratio and low communication overhead without using any topological information, thereby providing scalability to the network size. Under a mild assumption, ST-BCAST satisfies two sufficient conditions for reliable broadcasting: first-hop delivery condition and successful relay condition. We verify the feasibility of tone-signal generation and detection through experiments using Universal Software Radio Peripheral (USRP) devices, and show through NS-3 simulations that ST-BCAST significantly outperforms the state-of-the-art broadcast schemes in terms of packet delivery ratio and communication overhead.

Cognitive Radio (CR) networks have received much attention as a solution to the spectrum inefficiency problem of current license-based regulatory management. In CR networks, Secondary User (SU) can use a spectrum vacancy that can be detected by either sensing-before-transmission or database access. However, sensing inaccuracy or long access time to database often becomes a major obstacle to timely detect the spectrum vacancy. In this paper, we develop a hybrid detection framework in multi-channel CR networks, where an SU can selectively sense a channel for spectrum vacancy by accessing the spectrum history of Markovian channels. We focus on the value of the channel history information offered by the Primary Provider (PP) of each channel, and consider a market for the information between multiple PPs and SU. We investigate the interplay between of the PPs and the SU through their pricing and buying decisions for the information, in the presence of sensing inaccuracy, i.e., false alarm and miss detection.

Bufferbloat has drawn much attention in the network community for its negative impact on TCP delay performance and user QoE. Recently, it has been more commonly noted in wireless access networks, in part, due to over-provisioned buffer space. Previous works that focused only on bufferbloat prevention have suffered from either deployment or fairness problems when coexisting with conventional TCP flows. In this paper, we address the bufferbloat problem in resource-competitive environments such as Wi-Fi, and design a receiver-side countermeasure for easy deployment that does not require any modification at the sender or intermediate routers. Exploiting TCP and AQM dynamics, our scheme competes for shared resource in a fair manner with conventional TCP flow control methods and prevents bufferbloat. We implement our proposed scheme in commercial smart devices and verify its performance through real experiments in LTE and Wi-Fi networks.

It has been known that scheduling algorithms designed to achieve throughput optimality and good delay performance often require solving the Maximum Weighted Independent Set (MWIS) problem. However, under most realistic network settings, the MWIS problem is known to be NP-hard. In non-fading environments, low-complexity scheduling algorithms have been provided that converge either to the MWIS solution in time or to a solution that achieves at least a provable fraction of the achievable throughput. However, in more practical systems the channel conditions can vary at faster time-scales than convergence occurs in these lower-complexity algorithms. Hence, these algorithms cannot take advantage of opportunistic gains, and may no longer result in achieving good performance. In this paper, we propose a low-complexity scheduling scheme that performs provably well under fading channels and is amenable to implement in a distributed manner. To the best of our knowledge, this is the first scheduling scheme under fading environments that requires only local information, has a low complexity that grows logarithmically with the network size (provided that the conflict graph has bounded maximum vertex degree), and achieves provable performance guarantees (arbitrarily close to that of the well-known centralized Greedy Maximal Scheduler). We verify that the throughput and the delay of our proposed scheme are close to those of the optimal MaxWeight that solves MWIS at each time. Further, we implement our algorithm in a testbed by modifying the existing IEEE 802.11 DCF. The experiment results show that our implementation successfully accounts for wireless fading, attains the short-term opportunistic gains in practice, and hence substantially outperforms IEEE 802.11 DCF.

In-band wireless full-duplex is a promising technology that enables a wireless node to transmit and receive at the same time on the same frequency spectrum. In OFDMA networks, the full-duplex transmission makes the resource allocation problem more challenging, in particular when user devices are not full-duplex capable. In this paper, we investigate the joint problem of subcarrier assignment and power allocation to maximize the sum-rate performance in full-duplex OFDMA networks. To achieve high throughput in the considered network, we propose to use a practical subcarrier assignment condition which allows a subcarrier to be allocated to a pair of uplink and downlink nodes when its inter-node channel gain is lower than its uplink channel gain. Considering this condition and the inter-node interference, we design three resource allocation algorithms which run for; i) uplink first, ii) downlink first, and iii) uplink and downlink in pair. Through simulation, we evaluate our solutions in comparison with conventional schemes with respect to performance gain.

Multi-Path TCP (MPTCP) has attracted much attention as a promising technology to improve throughput performance of wireless devices that support multi-homed heterogeneous networks. Although MPTCP provides significant increase in network capacity, it may suffer from poor delay performance since the delay tends to be aligned with the worst-performing path: packets delivered through a short-delay subflow have to wait in the reordering buffer for packets being transmitted over a long-delay subflow. In this paper, we investigate the application-level delay performance of streaming traffic over MPTCP, and develop an analytical framework to take into account non-negligible network queuing delay and the interplay of congestion control between multiple subflows. We design a simple threshold-based subflow traffic allocation scheme that aims to minimize user-level delay and develop a receiver-centric traffic splitting control (R-TSC) that can be tuned to user preferences. The client-side R-TSC solution facilitates incremental deployment of low-delay streaming service over MPTCP. Through simulation and testbed experiments using commercial LTE and WiFi networks, we demonstrate significant performance gains over the standard MPTCP protocol.

Recent advances in the physical layer have demonstrated the feasibility of in-band wireless full-duplex which enables a node to transmit and receive simultaneously on the same frequency band. While the full-duplex operation can ideally double the spectral efficiency, the network-level gain of full-duplex in large-scale networks remains unclear due to the complicated resource allocation in multi-carrier and multi-user environments. In this paper, we consider a single-cell full-duplex OFDMA network which consists of one full-duplex base station (BS) and multiple full-duplex mobile nodes. Our goal is to maximize the sum-rate performance by jointly optimizing subcarrier assignment and power allocation considering the characteristics of full-duplex transmissions. We develop an iterative solution that achieves local Pareto optimality in typical scenarios. Through extensive simulations, we demonstrate that our solution empirically achieves near-optimal performance and outperforms other resource allocation schemes designed for half-duplex networks. Also, we reveal the impact of various factors such as the channel correlation, the residual self-interference, and the distance between the BS and nodes on the full-duplex gain.

MPTCP is a promising transport technique to boost throughput of wireless multi-homed device by supporting multiple concurrent transmissions through heterogeneous wireless interfaces. As the number of concurrent subflows increase, MPTCP can achieve a linearly increasing throughput performance, but it is unclear how much improvement in the end-to-end delay performance can be attained from additional subflows. In this paper, we develop an analytical framework to understand the end-to-end delay performance of MPTCP that accounts for TCP dynamics and subflow interactions. Interestingly, it turns out that the delay performance can be even degraded with additional subflows. Considering MPTCP in heterogeneous wireless networks of Wi-Fi and LTE, we formulate a cost minimization problem subject to the end-to-end delay constraint. Based on the insight obtained from our model, we approximate the problem and develop a greedy scheme that splits traffic to minimize the cost while satisfying the delay constraints. Through simulations, we demonstrate that our proposed scheme outperforms the conventional MPTCP, and significantly improves the delay performance while lowering the user cost.

In this paper, we investigate the utility maximization problem for a sensor network with energy replenishment. Each sensor node consumes energy in its battery to generate and deliver data to its destination via multihop communications. Although the battery can be replenished from renewable energy sources, the energy allocation should be carefully designed in order to maximize system performance, especially when the replenishment profile is unknown in advance. In this paper, we address the joint problem of energy allocation and routing to maximize the total system utility, without prior knowledge of the replenishment profile. We first characterize optimal throughput of a single node under general replenishment profile and extend our idea to the multihop network case. After characterizing the optimal network utility with an upper bound, we develop a low-complexity online solution that achieves asymptotic optimality. Focusing on long-term system performance, we can greatly simplify computational complexity while maintaining high performance. We also show that our solution can be approximated by a distributed algorithm using standard optimization techniques. In addition, we show that the required battery size is O(ln(1/ξ)) to constrain the performance of our scheme within ξ-neighborhood of the optimum. Through simulations with replenishment profile traces for solar and wind energy, we numerically evaluate our solution, which outperforms a state-of-the-art scheme that is developed based on the Lyapunov optimization technique.

Link adaptation technologies, such as Adaptive Modulation and Coding (AMC) and Multiple-Input-Multiple-Output (MIMO), are used in advanced wireless communication systems to achieve high spectrum efficiency. Communication performance can be improved significantly by adaptive transmissions based on the quality of received signals, i.e., the signal-to-interference-plus-noise ratio (SINR). However, for multihop wireless communications, most link scheduling schemes have been developed under simplified interference models that do not account for accumulative interference and cannot fully exploit the recent advances in PHY-layer communication theory. This paper focuses on developing link scheduling schemes that can achieve optimal performance under the SINR model. One key idea is to treat an adaptive wireless link as multiple parallel virtual links with different signal quality, building on which we develop throughput-optimal scheduling schemes using a two-stage queueing structure in conjunction with recently developed carrier-sensing techniques. Furthermore, we introduce a novel three-way handshake to ensure, in a distributed manner, that all transmitting links satisfy their SINR requirements. We evaluate the proposed schemes through rigorous analysis and simulations.

In this paper, we study the implication of wireless broadcast for data aggregation in lossy wireless sensor networks. Each sensor node generates information by sensing its physical environment and transmits the data to a special node called the sink, via multihop communications. The goal of the network system is to compute a function at the sink from the information gathered by spatially distributed sensor nodes. In the course of collecting information, in-network computation at intermediate forwarding nodes can substantially increase network efficiency by reducing the number of transmissions. On the other hand, it also increases the amount of the information contained in a single packet and makes the system vulnerable to packet loss. Instead of retransmitting lost packets, which incurs additional delay, we develop a wireless system architecture that exploits the diversity of the wireless medium for reliable operations. To elaborate, we show that for a class of aggregation functions, wireless broadcasting is an effective strategy to improve delay performance while satisfying reliability constraint. We provide scaling law results on the performance improvement of our solution over unicast architecture with retransmissions. Interestingly, the improvement depends on the transmission range as well as the reliability constraint.

Super Wi-Fi is a Wi-Fi like service over TV white spaces (TVWS) based on the dynamic spectrum access (DSA) technology. Although Super Wi-Fi is expected to achieve larger coverage than conventional Wi-Fi thanks to the superior propagation characteristics of TVWS, it suffers from smaller bandwidth than Wi-Fi (6–8 MHz versus 20 MHz) which degrades network capacity. Therefore, it is common belief that the two Wi-Fi technologies may target different applications such as Super Wi-Fi for coverage and Wi-Fi for speed. However, there is a lack of studies that rigorously analyzes and compares the performance of Super Wi-Fi and Wi-Fi to confirm such belief. To fill the gap, this paper performs a thorough analysis on the capacity of Super Wi-Fi under the scenario that a Super Wi-Fi access point (AP) coexists with a Wi-Fi AP. Comparing the downlink capacity of Super Wi-Fi and Wi-Fi reveals that Super Wi-Fi can outperform Wi-Fi at the outskirts of the Wi-Fi's coverage and Super Wi-Fi gets more beneficial when channel bonding is employed. In addition, the maximal coverage radius of Super Wi-Fi is derived with which Super Wi-Fi can achieve better average capacity than a network of densely-deployed Wi-Fi APs, where the maximal radius is up to 3.2 times larger than the coverage radius of Wi-Fi.

In this paper, we focus on the issue of stability in multihop wireless networks under flow-level dynamics, and explore the inefficiency and instability of the celebrated Back-Pressure algorithms. It has been well-known that the Back-Pressure (or Max-Weight) algorithms achieve queue stability and throughput optimality in a wide variety of scenarios. Yet, these results all rely on the assumptions that the set of flows is fixed, and that all the flows are long-lived and keep injecting packets into the network. Recently, in the presence of flow-level dynamics, where flows arrive and request to transmit a finite amount of packets, it has been shown that the Max-Weight algorithms may not guarantee stability due to channel fading or inefficient spatial reuse. However, these observations are made only for single-hop traffic, and thus have resulted in partial solutions that are limited to the single-hop scenarios. An interesting question is whether straightforward extensions of the previous solutions to the known instability problems would achieve throughput optimality in multihop traffic setting. To answer the question, we explore potential inefficiency and instability of the Back-Pressure algorithms, and provide interesting examples that are useful to obtain insights into developing an optimal solution. We also conduct simulations to further illustrate the instability issue of the Back-Pressure algorithms in various scenarios. Our study reveals that new types of inefficiencies may arise in the settings with multihop traffic due to underutilization of the link capacity or inefficient routing, and the stability problem becomes more challenging than in the single-hop traffic counterpart.

Developing scheduling mechanisms that can simultaneously achieve throughput optimality and good delay performance often require solving the Maximum Independent Weighted Set (MWIS) problem. However, under most realistic network settings, the MWIS problem can be shown to be NP-hard. In non-fading environments, low-complexity scheduling algorithms have been provided that converge either to the MWIS solution in time or to a solution that achieves at least a provable fraction of the achievable throughput. However, in more practical systems the channel conditions can vary at faster time-scales than convergence occurs in these lower-complexity algorithms. Hence, these algorithms cannot take advantage of the opportunistic gain, and may no longer guarantee good performance. In this paper, we propose a low-complexity scheduling scheme that performs provably well under fading channels and is amenable to implement in a distributed manner. To the best of our knowledge, this is the first scheduling scheme under fading environments that requires only local information, has a low complexity that grows logarithmically with the network size, and achieves provable performance guarantees (which is arbitrarily close to that of the well-known centralized Greedy Maximal Scheduler). Through simulations we verify that both the throughput and the delay under our proposed distributed scheduling scheme are close to that of the optimal solution to MWIS. Further, we implement a preliminary version of our algorithm in a testbed by modifying the existing IEEE 802.11 DCF. The preliminary experiment results show that our implementation successfully accounts for wireless fading, and attains the opportunistic gains in practice, and hence substantially outperforms IEEE 802.11 DCF.

In this paper, we develop distributed random access scheduling schemes that exploit the time-varying nature of fading channels for multimedia traffic in multihop wireless networks. It should be noted that while centralized scheduling solutions can achieve optimal throughput under this setting, they incur high-computational complexity and require centralized coordination requiring global channel information. The proposed solution not only achieves provable performance guarantees under a wide range of interference models, but also can be implemented in a distributed fashion using local information. To the best of our knowledge, this is the first distributed scheduling mechanism for fading channels that achieves provable performance guarantees. We show through simulations that the proposed schemes achieve better empirical performance than other known distributed scheduling schemes.

The problem of developing distributed scheduling algorithms for high throughput in multihop wireless networks has been extensively studied in recent years. The design of a distributed low-complexity scheduling algorithm becomes even more challenging when taking into account a physical interference model, which requires the SINR at a receiver to be checked when making scheduling decisions. To do so, we need to check whether a transmission failure is caused by interference due to simultaneous transmissions from distant nodes. In this paper, we propose a scheduling algorithm under a physical interference model, which is amenable to distributed implementation with 802.11 CSMA technologies. The proposed scheduling algorithm is shown to achieve throughput optimality. We present two variations of the algorithm to enhance the delay performance and to reduce the control overhead, respectively, while retaining throughput optimality.

In this paper, we consider the problem of link scheduling in multihop wireless networks under general interference constraints. Our goal is to design scheduling schemes that do not use per-flow or per-destination information, maintain a single data queue for each link, and exploit only local information, while guaranteeing throughput optimality. Although the celebrated back-pressure algorithm maximizes throughput, it requires per-flow or per-destination information. It is usually difficult to obtain and maintain this type of information, especially in large networks, where there are numerous flows. Also, the back-pressure algorithm maintains a complex data structure at each node, keeps exchanging queue-length information among neighboring nodes, and commonly results in poor delay performance. In this paper, we propose scheduling schemes that can circumvent these drawbacks and guarantee throughput optimality. These schemes use either the readily available hop-count information or only the local information for each link. We rigorously analyze the performance of the proposed schemes using fluid limit techniques via an inductive argument and show that they are throughput-optimal. We also conduct simulations to validate our theoretical results in various settings and show that the proposed schemes can substantially improve the delay performance in most scenarios.

Scheduling is a critical and challenging resource allocation mechanism for multihop wireless networks. It is well known that scheduling schemes that favor links with larger queue length can achieve high throughput performance. However, these queue-length-based schemes could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets belonging to some flows may be excessively delayed due to lack of subsequent packet arrivals. Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. However, characterizing throughput optimality of these delay-based schemes has largely been an open problem in multihop wireless networks (except in limited cases where the traffic is single-hop.) In this paper, we investigate delay-based scheduling schemes for multihop traffic scenarios with fixed routes. We develop a scheduling scheme based on a new delay metric and show that the proposed scheme achieves optimal throughput performance. Furthermore, we conduct simulations to support our analytical results and show that the delay-based scheduler successfully removes excessive packet delays, while it achieves the same throughput region as the queue-length-based scheme.

In this paper, we study distributed scheduling in multihop multiple-input–multiple-output (MIMO) networks. We first develop a “MIMO-pipe” model that provides the upper layers a set of rates and signal-to-interference-plus-noise ratio (SINR) requirements that capture the rate–reliability tradeoff in MIMO communications. The main thrust of this paper is then dedicated to developing distributed carrier sense multiple access (CSMA) algorithms for MIMO-pipe scheduling under the SINR interference model. We choose the SINR model over the extensively studied protocol-based interference models because it more naturally captures the impact of interference in wireless networks. The coupling among the links caused by the interference under the SINR model makes the problem of devising distributed scheduling algorithms very challenging. To that end, we explore the CSMA algorithms for MIMO-pipe scheduling from two perspectives. We start with an idealized continuous-time CSMA network, where control messages can be exchanged in a collision-free manner, and devise a CSMA-based link scheduling algorithm that can achieve throughput optimality under the SINR model. Next, we consider a discrete-time CSMA network, where the message exchanges suffer from collisions. For this more challenging case, we develop a “conservative” scheduling algorithm by imposing a more stringent SINR constraint on the MIMO-pipe model. We show that the proposed conservative scheduling achieves an efficiency ratio bounded from below.

In this paper, we investigate the utility maximization problem for a sensor network with energy replenishment. Each sensor node consumes energy in its battery to generate and deliver data to its destination via multi-hop communications. Although the battery can be replenished from renewable energy sources, the energy allocation should be carefully designed in order to maximize system performance, especially when the replenishment profile is unknown in advance. In this paper, we address the joint problem of energy allocation and routing to maximize the total system utility, without prior knowledge of the replenishment profile. We first characterize optimal throughput of a single node under general replenishment profile, and extend our idea to the multi-hop network case. After characterizing the optimal network utility with an upper bound, we develop a low-complexity online solution that achieves asymptotic optimality. Focusing on long-term system performance, we can greatly simplify computational complexity while maintaining high performance. We also show that our solution can be approximated by a distributed algorithm using standard optimization techniques. Through simulations with replenishment profile traces for solar and wind energy, we numerically evaluate our solution, which outperforms a state-of-the-art scheme that is developed based on the Lyapunov optimization technique.

We consider the problem of joint link scheduling and power control for wireless networks with average transmission power constraints. Due to the high computational complexity of the optimal policies, we extend the class of greedy link scheduling policies to handle average power constraints. We develop a greedy link scheduling and power control scheme GECS, with provable performance guarantees. We show that the performance of our greedy scheduler can be characterized using the Local Pooling Factor (LPF) of a network graph, which has been previously used to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for wireless networks. We also simulate the performance of GECS on wireless network, and compare its performance to another candidate greedy link scheduling and power control policy.

In recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multihop wireless networks. A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because its empirically observed performance is close to optimal in a variety of network settings. However, its distributed realization requires high complexity, which becomes a major obstacle for practical implementation. In this paper, we develop simple distributed greedy algorithms for scheduling in multihop wireless networks. We reduce the complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulation results show that the new algorithms approximate the performance of GMS, and outperform the state-of-the-art distributed scheduling policies.

In this paper, we investigate the problem of maximizing the throughput over a finite-horizon time period for a sensor network with energy replenishment. The finite-horizon problem is important and challenging because it necessitates optimizing metrics over the short term rather than metrics that are averaged over a long period of time. Unlike the infinite-horizon problem, the fact that inefficiencies cannot be made to vanish to infinitesimally small values, means that the finite-horizon problem requires more delicate control. The finite-horizon throughput optimization problem can be formulated as a convex optimization problem, but turns out to be highly complex. The complexity is brought about by the “time coupling property,” which implies that current decisions can influence future performance. To address this problem, we employ a three-step approach. First, we focus on the throughput maximization problem for a single node with renewable energy assuming that the replenishment rate profile for the entire finite-horizon period is known in advance. An energy allocation scheme that is equivalent to computing a shortest path in a simply-connected space is developed and proven to be optimal. We then relax the assumption that the future replenishment profile is known and develop an online algorithm. The online algorithm guarantees a fraction of the optimal throughput. Motivated by these results, we propose a low-complexity heuristic distributed scheme, called NetOnline, in a rechargeable sensor network. We prove that this heuristic scheme is optimal under homogeneous replenishment profiles. Further, in more general settings, we show via simulations that NetOnline significantly outperforms a state-of-the-art infinite-horizon based scheme, and for certain configurations using data collected from a testbed sensor network, it achieves empirical performance close to optimal.

Scheduling is a critical and challenging resource allocation mechanism for multi-hop wireless networks. It is well known that scheduling schemes that give a higher priority to the link with larger queue length can achieve high throughput performance. However, this queue-length-based approach could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals. Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered. However, the throughput performance of delay-based schemes has largely been an open problem except in limited cases of single-hop networks. In this paper, we investigate delay-based scheduling schemes for multi-hop traffic scenarios. We view packet delays from a different perspective, and develop a scheduling scheme based on a new delay metric. Through rigorous analysis, we show that the proposed scheme achieves the optimal throughput performance. Finally, we conduct extensive simulations to support our analytical results, and show that the delay-based scheduler successfully removes excessive packet delays, while it achieves the same throughput region as the queue-length-based scheme.

This paper focuses on designing and analyzing throughput-optimal scheduling policies that avoid using per-flow or per-destination information, maintain a single data queue for each link, exploit only local information, and potentially improve the delay performance, for multi-hop wireless networks under general interference constraints. Although the celebrated backpressure algorithm maximizes throughput, it requires per-flow or per-destination information (which may be difficult to obtain and maintain), maintains per-flow or per-destination queues at each node, relies on constant exchange of queue length information among neighboring nodes to calculate link weights, and may result in poor delay performance. In contrast, the proposed schemes can circumvent these drawbacks while guaranteeing throughput optimality. We rigorously analyze the throughput performance of the proposed schemes and show that they are throughput-optimal using fluid limit techniques via an inductive argument. We also conduct simulations to show that the proposed schemes can substantially improve the delay performance.

Recently, significant advances have been made in wireless scheduling toward high-performance networks, leading to development of throughput-optimal scheduling schemes. Beyond throughput performance, however, scheduling with good delay performance has remained open except for a small class of network systems. In this paper, we extend the well-known back-pressure scheduling scheme by using logarithmic weight and improve the delay performance without any loss of throughput performance under multi-hop traffic. We provide rigorous analysis for throughput performance of the proposed solution, and evaluate delay performance through simulations.

In-network aggregation has become a promising technique for improving the energy efficiency of wireless sensor networks. Aggregating data at various nodes in the network results in a reduction in the amount of bits transmitted over the network, and hence, saves energy. In this paper, we focus on another important aspect of aggregation, i.e., delay performance. In conjunction with link scheduling, in-network aggregation can reduce the delay by lessening the demands for wireless resources and thus expediting data transmissions. We formulate the problem that minimizes the sum delay of sensed data, and analyze the performance of optimal scheduling with in-network aggregation in tree networks under the node-exclusive interference model. We provide a system wide lower bound on the delay and use it as a benchmark for evaluating different scheduling policies. We numerically evaluate the performance of myopic and non-myopic scheduling policies, where myopic one considers only the current system state for a scheduling decision while non-myopic one simulates future system states. We show that the one-step non-myopic policies can substantially improve the delay performance. In particular, the proposed non-myopic greedy scheduling achieves a good tradeoff between performance and implementability.

We investigate the performance of longest-queue-first (LQF) scheduling (i.e., greedy maximal scheduling) for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless interference more precisely than binary interference models. By employing the ρ-local pooling technique, we show that LQF scheduling achieves zero throughput in the worst case. We then propose a novel technique to localize interference which enables us to decentralize the LQF scheduling while preventing it from having vanishing throughput in all network topologies. We characterize the maximum throughput region under interference localization and present a distributed LQF scheduling algorithm. Finally, we present numerical results to illustrate the usefulness and to validate the theory developed in the paper.

The problem of developing high-performance distributed scheduling algorithms for multi-hop wireless networks has seen enormous interest in recent years. The problem is especially challenging when studied under a physical interference model, which requires the SINR at the receiver to be above a certain threshold for decoding success. Under such an SINR model, transmission failure may be caused by interference due to simultaneous transmissions from far away nodes, which exacerbates the difficulty in developing a distributed algorithm. In this paper, we propose a scheduling algorithm that exploits carrier sensing and show that the algorithm is not only amenable to distributed implementation, but also results in throughput optimality. Our algorithm has a feature called the dual-state approach, which separates the transmission schedules from the system state and can be shown to improve delay performance.

Two-tier overlay networks that consist of a conventional macrocell network and femtocell hotspots offer an economical solution for high user capacity and extended coverage. However, wireless interference across tiers causes significant performance degradation and restricts spectrum reuse. In this paper, we explore schemes to mitigate cross-tier interference with beamforming antennas for overlay networks. In our model, femtocells can operate with frequency spectrum that is either shared with or separated from the macrocell. The enhanced SIR from beamforming contributes to the population of femtocells with the shared spectrum, and thus improve the spectrum efficiency. Given a required SIR level, we show that which femtocells can use the shared spectrum and how much spectrum can be shared to maximize total utility. We show through a numerical performance evaluation that proposed schemes improve spectrum utilization for two-tier overlay networks.

In this paper, we characterize the performance of an important class of scheduling schemes, called greedy maximal scheduling (GMS), for multi-hop wireless networks. While a lower bound on the throughput performance of GMS is relatively well-known in the simple node-exclusive interference model, it has not been thoroughly explored in the more general K-hop interference model. Moreover, empirical observations suggest that the known bounds are quite loose, and that the performance of GMS is often close to optimal. In this paper, we provide a number of new analytic results characterizing the performance limits of GMS. We first provide an equivalent characterization of the efficiency ratio of GMS through a topological property called the local-pooling factor of the network graph. We then develop an iterative procedure to estimate the local-pooling factor under a large class of network topologies and interference models. We use these results to study the worst-case efficiency ratio of GMS on two classes of network topologies. First, we show how these results can be applied to tree networks to prove that GMS achieves the full capacity region in tree networks under the K-hop interference model. Second, we show that the worst-case efficiency ratio of GMS in geometric network graphs is between 1/6 and 1/3.

In recent years, there have been many efforts to develop low-complexity scheduling schemes that can approximate optimal performance in multi-hop wireless networks. A centralized sub-optimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because it achieves high throughput. However, its distributed realization requires O(|V|) complexity, which becomes a major obstacle for practical implementation, where |V| is the number of nodes in the network. In this paper, we develop a simple distributed scheduling policy for multi-hop wireless networks. It achieves O(log |V|) complexity by relaxing the global ordering requirement of GMS. Instead, it deterministically schedules only links that have the largest queue length among their local neighbors. We show that, it still guarantees a fraction of the optimal performance, which is no smaller than GMS. We also further improve its performance and address some important implementation issues. The simulation results confirm that the new scheduling scheme achieves the performance equivalent of GMS and significantly outperforms state-of-the-art distributed random access scheduling policies.

The performance of scheduling schemes in multi-hop wireless networks has attracted significant attention in the recent literature. It is well known that optimal scheduling solutions require centralized information and lead to impractical implementations due to their enormous complexity (high-degree polynomial or NP-hard, depending on the interference scenario). Further, multi-hop networks typically require distributed algorithms that operate on local information. Thus, in this paper, we develop a constant-time distributed random access algorithm for scheduling in multi-hop wireless networks. An important feature of this scheme is that it is guaranteed to achieve a fraction (efficiency factor) of the optimal performance. We show that this scheme theoretically achieves a superior efficiency factor as well as numerically achieves a significant performance improvement over the state-of-the-art. Simulation results also confirm that the performance of this scheme is close to a greedy centralized scheme.

While TCP is highly successful in the wire-line Internet, its performance fast degrades as the number of hops increases in multihop wireless networks. It is due to not only the half-duplex nature of the wireless medium, but also the congestion spreading phenomenon. Congestion in one wireless link spreads over space rather than localized to a link, causing interference to packet transmissions on neighboring links. Therefore, the space-shared feature of multihop wireless network makes congestion control different from that in wired networks. Since TCP often errs in estimating congestion level due to the wireless interference, it can overly inflate the transmission window and blast packets into the network, resulting in high level of congestion. We propose a novel algorithm to detect congestion in multihop wireless networks, which enables TCP to adjust the window size precisely. Performance evaluation through simulations confirms the advantage of our proposal in detecting spatial congestion in multihop wireless networks.

Loss differentiation is recommended as a service differentiation provided by an assured forwarding (AF) per-fop behavior (PHB) in differentiated service (DiffServ) architecture. An active queue management (AQM) technique is addressed as a suitable alternative to realize the service differentiation because the AF PHB should attempt to minimize long-term congestion while permitting short-term congestion in order to accommodate traffic bursts. In order to realize the AF PHB using an AQM scheme, it is desirable that the AQM scheme has DVO properties of sheltering and load tolerance in order to protect low drop precedence traffic and to prevent starvation of high drop precedence traffic. In this paper, we introduce another desirable property of assured drop probability. We modify an existing AQM algorithm for the property so that it assures a target drop probability in a properly provisioned network. Other properties of sheltering and load tolerance still hold for the modified AQM scheme. We evaluate it with other comparable schemes, i.e., WRED and RIO through simulation.

We propose a new AQM algorithm that considers both the average queue length and the estimated packet arrival rate together in order to detect and control incipient congestion. It predicts the average queue length and controls it to maintain a certain reference value to achieve high link utilization and low queueing delay. Simulation results confirm the stability of our proposed algorithm under various network environments and show its performance advantages over other competitive AQM algorithms.

AQM attempts to provide high network utilization with low loss and delay by regulating queues at bottleneck links. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of non-intuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm that combines the more effective elements of recent algorithms with a RED core. Throughput analysis and simulations, we demonstrate improved performance in stability and response time with straightforward selection parameters for both steady load and changes in loads.