Towards wearable cognitive assistance

By Kiryong Ha, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, Mahadev Satyanarayanan. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2014

530 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/776ef127a840820f4fb575024f76244de44c206d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xu Chen
2. Giuseppe Aceto
3. Jiasi Chen
4. Xukan Ran
5. Valerio Persico
6. Wei Gao
7. Antonio Pescapè
8. Zhi Zhou

In Computer Vision and Pattern Recognition

1. Jiasi Chen
2. Xukan Ran
3. Xu Chen
4. Liekang Zeng
5. Zhi Zhou
6. Atay Ozgovde
7. Ahmet Cihat Baktir
8. Cem Ersoy

In Human-Computer Interaction

1. Jiasi Chen
2. Carlos Bermejo
3. Cem Ersoy
4. Lin Zhong
5. Xukan Ran
6. Atay Ozgovde
7. Zhanpeng Huang
8. Dimitris Chatzopoulos

Citations in the corpus

• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019, background): “These efforts have boosted a wide spectrum of AI applications, spanning from live video analytics [12], cognitive assistance [13] to precision agriculture, smart home [14], and Industrial Internet of Things (IIoT) [15].”
• MarkIt: privacy markers for protecting visual secrets (2014, background): “The idea is to offload heavy computation on server/cloud infrastructure [5, 7, 8, 12].”
• Computation Offloading Toward Edge Computing (2019, background): “, traffic control [151], self-driving cars [152], personal cognitive assistance [153], surveillance and security [154], and AR/VR [155]–[157].”
• Distributed data stream processing and edge computing: A survey on resource elasticity and future directions (2017, unknown): “Emerging cognitive assistance scenarios [3] offer interesting use cases where machine learning models can be trained on the cloud, and once trained they can be deployed on edge computing”
• Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “Regarding the latency requirements, although to the best of our knowledge there is no empirical academic study on the impact of delays for Augmented Reality, several studies measure latency for various offloading scenarios in MAR systems [13], [243], [244].”
• AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, background): “[38] have proposed an architecture for offloading Google Glass workloads to nearby cloudlets.”
• DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics (2018, background): “[25] offloads processing from Google Glass to nearby cloudlets.”
• Security and Privacy Issues of Fog Computing: A Survey (2015, background): “End devices, fog and cloud are forming a three layer hierarchical service delivery model, supporting a range of applications such as web content delivery [48], augmented reality [15], and big data analysis [46].”
• Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices (2015, background): “Offloading is a well-known idea [13, 23, 22], but in the context of continuous recognition, it must be applied with care because wireless network latencies are too high.”
• DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications (2017, methodology): “Gabriel [24] uses cloudlets to support cognitive assistance applications while LiKamWa et al.”
• Fog Computing: Platform and Applications (2015, background): “References [11], [13], [14], [18], [22], [24] are about how exiting or new applications and services can benefit from fog computing.”
• An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “Figure 3: Cognitive assistance application [65], shown in (a), and deployed onto Fog network, shown in (b).”
• MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints (2016, background): “Off-loading from mobile device to cloud has long been an option to handle heavyweight computations [16, 19, 38].”
• A hierarchical edge cloud architecture for mobile computing (2016, background): “Such remote execution benefits a large varieties of mobile applications, such as gesture recognition [8], voice control, recognition assistance [2] and mobile gaming [6].”
• Application-aware traffic scheduling for workload offloading in mobile clouds (2016, background): “Modern mobile devices continuously generate and process sensory data about the surrounding environment, and hence enable users’ contextual awareness [12], [6].”
• A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, unknown): “[152] considered a wearable cognitive assistance”
• Edge Computing: Vision and Challenges (2016, unknown): “[21] used cloudlets to offload computing tasks for wearable cognitive assistance,”