Back Deep Sleep Save as PDF
Chapters (72)
  1. TITLE PAGE
  2. COPYRIGHT PAGE
  3. DEDICATION
  4. CONTENTS
  5. PROLOGUE
  6. PART I
  7. CHAPTER 1
  8. CHAPTER 2
  9. CHAPTER 3
  10. CHAPTER 4
  11. CHAPTER 5
  12. CHAPTER 6
  13. CHAPTER 7
  14. PART II
  15. CHAPTER 8
  16. CHAPTER 9
  17. CHAPTER 10
  18. CHAPTER 11
  19. CHAPTER 12
  20. CHAPTER 13
  21. CHAPTER 14
  22. CHAPTER 15
  23. CHAPTER 16
  24. CHAPTER 17
  25. CHAPTER 18
  26. PART III
  27. CHAPTER 19
  28. CHAPTER 20
  29. CHAPTER 21
  30. CHAPTER 22
  31. CHAPTER 23
  32. CHAPTER 24
  33. CHAPTER 25
  34. CHAPTER 26
  35. CHAPTER 27
  36. CHAPTER 28
  37. CHAPTER 29
  38. CHAPTER 30
  39. CHAPTER 31
  40. PART IV
  41. CHAPTER 32
  42. CHAPTER 33
  43. CHAPTER 34
  44. CHAPTER 35
  45. CHAPTER 36
  46. CHAPTER 37
  47. CHAPTER 38
  48. CHAPTER 39
  49. CHAPTER 40
  50. CHAPTER 41
  51. PART V
  52. CHAPTER 42
  53. CHAPTER 43
  54. CHAPTER 44
  55. CHAPTER 45
  56. CHAPTER 46
  57. CHAPTER 47
  58. CHAPTER 48
  59. CHAPTER 49
  60. CHAPTER 50
  61. CHAPTER 51
  62. CHAPTER 52
  63. CHAPTER 53
  64. CHAPTER 54
  65. CHAPTER 55
  66. CHAPTER 56
  67. PART VI
  68. CHAPTER 57
  69. CHAPTER 58
  70. CHAPTER 59
  71. ACKNOWLEDGMENTS
  72. ABOUT THE AUTHOR

“Can’t hurt to know where everyone is staying,” said Farrington.

“It’s a block past this next traffic light,” said Gupta. “I’ll run the license plate identification application in live-match mode and see if we pick up any other vehicles from the coffee shop or Whole Foods.”

When activated, the application automatically recorded license plates captured by the Suburban’s cameras, compiling them in a database by time and location filmed, state, and plate number, which they could analyze later. It also ran newly recorded plates through the entire database, looking for matches. The application would allow them to drive through hotel parking lots and identify a vehicle they might have missed while visually surveilling the morning’s other areas of interest.

It also gave them the capability to detect a tail. While driving, they could capture the license plates in front and behind them for a predetermined amount of time. The heavier the traffic, the longer the camera needed to grab all the plates. It would zoom out as far as possible to accomplish this. Thirty minutes later, after executing a few soft evasion techniques—such as slowing down or running a repetitive street-grid pattern—they could reactivate the application with the live-match feature enabled, and identify every vehicle that had stuck with them.

“Scott. Why don’t you climb in back so you don’t get made, just in case they’re having a meetup in the parking lot,” said Farrington.

“Good thinking,” said Daly before leaning his seat back as far as it would go and crawling over it.

Daly returned the seat to its upright position as Farrington turned into the Hilton Garden Inn parking lot. A long driveway passed underneath the hotel’s covered entrance, paralleling the hotel until it opened into the parking lot situated behind the building. He spotted the black Outback immediately, its owner still seated behind the wheel. The man glanced through the driver’s-side window as the Suburban passed but didn’t seem to take interest. Thankfully, the lot was nearly full, the two spaces remaining clearly too tight for his oversize vehicle. He made an attempt to park in one of them, lining up a few times before driving away, to demonstrate to the Outback’s driver that he’d tried.

“Was he watching me the whole time?” asked Farrington.

“No. He went back to looking at his phone as soon as you passed by,” said Gupta. “Looked up once after that, but just for a moment.”

“Thank god the lot was full,” said Farrington. “I wasn’t expecting it to be this small, with one way in or out.”

“That’s my fault,” said Gupta. “It looked like it connected to the street behind it on Google Maps, or I would have said something.”

“Any hits on any other vehicles?” asked Farrington. “I didn’t see the Durango or Pathfinder.”